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TD 225 
. C43 
fl47 
2003 
Copy 1 


I 


United States Region III Region III EPA 903-R-03-002 

Environmental Protection Chesapeake Bay Water Protection April 2003 

Agency Program Office Division 

In coordination with the Office of Water/Office of Science and Technology, Washington, DC 



Ambient Water Quality 
Criteria for Dissolved 
Oxygen, Water Clarity and 
Chlorophyll a for the 
Chesapeake Bay and Its 
Tidal Tributaries 

f v ". ... 

April 2003 


f 



7b 32$ 
'CWM 7 

C^f 1 / • 

h v 


Ambient Water Quality Criteria 
for Dissolved Oxygen, Water Clarity 
and Chlorophyll a for the Chesapeake Bay 

and Its Tidal Tributaries 


April 2003 

U.S. Environmental Protection Agency 
Region III 

Chesapeake Bay Program Office 
Annapolis, Maryland 

and 

Region III 

Water Protection Division 
Philadelphia, Pennsylvania 

in coordination with 

Office of Water 

Office of Science and Technology 
Washington, D.C. 




LC Control Number 



2006 361756 












Contents 


Foreword. vii 

Executive Summary. ix 

Notices.xix 

Acknowledgments .xxi 

I. Introduction . 1 

National Criteria . 2 

Regional Nutrient Criteria. 2 

Chesapeake Bay Criteria. 3 

II. Chesapeake Bay Nutrient and Sediment Enrichment Criteria ... 5 

III. Dissolved Oxygen Criteria. 7 

Background . 7 

Chesapeake Bay science . 7 

Natural dissolved oxygen processes . 8 

Chesapeake Bay oxygen dynamics . 8 

Low dissolved oxygen: historical and recent past. 10 

Approach to Deriving Dissolved Oxygen Criteria. 12 

Chesapeake Bay dissolved oxygen restoration goal framework .... 14 
Regionalizing the EPA Virginian Province 

saltwater dissolved oxygen criteria . 15 

Applying the EPA freshwater dissolved oxygen criteria. 25 

Species listed as threatened or endangered. 27 

Scientific literature findings. 33 

Instantaneous minimum versus daily mean. 33 

Strengths and limitations of the criteria derivation procedures. 34 

Chesapeake Bay Dissolved Oxygen Criteria Derivation . 40 

Migratory fish spawning and nursery designated use criteria. 42 

Open-water fish and shellfish designated use criteria. 46 

Deep-water seasonal fish and shellfish designated use criteria. 52 

Deep-channel seasonal refuge designated use criteria. 60 


Library of Congress 





























Chesapeake Bay Dissolved Oxygen Criteria . 65 

Literature Cited. 67 

IV. Water Clarity Criteria . 81 

Background . 81 

Approach . 82 

The relationships between water quality, light and 

underwater bay grasses . 82 

Determining light requirements. 84 

Strengths and limitations of the criteria derivation procedures. 85 

Water Clarity Criteria Derivation. 90 

Minimum light requirements. 90 

Light-through-water requirements. 95 

Chesapeake Bay Water Clarity Criteria. 96 

r 

Literature Cited. 97 

V. Chlorophyll a Criteria .101 

Background .101 

Scope and magnitude of nutrient enrichment in Chesapeake Bay . . 101 
Chlorophyll a: key indicator of phytoplankton biomass.102 

Chesapeake Bay Chlorophyll a Criteria.104 

Supporting Technical Information and Methodologies.105 

Context for the narrative Chesapeake Bay chlorophyll a criteria ... 105 
Chlorophyll a concentrations characteristic of 

various ecological conditions.107 

Chlorophyll a concentrations characteristic of 

trophic-based conditions .129 

Chlorophyll a concentrations protective against 

water quality impairments.132 

Methodologies for deriving waterbody-specific 

chlorophyll a criteria .134 

Literature Cited.137 

VI. Recommended Implementation Procedures.143 

Defining Criteria Attainment.144 

Dissolved oxygen criteria.144 

Water clarity criteria.144 

Chlorophyll a criteria.147 

Contents 






























Addressing Magnitude, Duration, Frequency, Space and Time.148 

Developing the Cumulative Frequency Distribution.152 

Step 1. Interpolation of water quality monitoring data.152 

Step 2. Comparison of interpolated water quality 

monitoring data to the appropriate criterion value.155 

Step 3. Identification of interpolator cells that 

exceed the criterion value'.156 

Step 4. Calculation of the cumulative probability 

of each spatial extent of exceedance .156 

Step 5. Plot of spatial exceedance vs. the cumulative frequency ... 159 

Diagnosing the Magnitude of Criteria Exceedance.164 

Defining the Reference Curve .166 

Strengths and limitations.166 

Approaches to defining reference curves .167 

Reference curves for dissolved oxygen criteria.168 

Reference curves for water clarity criteria.171 

Reference curves for chlorophyll a criteria.174 

Reference curve implementation.174 

Monitoring to Support the Assessment of Criteria Attainment.176 

Shallow-water monitoring .176 

Dissolved oxygen criteria assessment.177 

Water clarity criteria assessment .185 

Chlorophyll a criteria assessment .191 

Evaluation of Chesapeake Bay Water Quality Model Output.194 

Chesapeake Bay Watershed Model .195 

Chesapeake Bay Water Quality Model .196 

Integration of Monitoring and Modeling for Criteria Assessment ..196 

Literature Cited.197 

VII. Diagnostic Procedures for Natural Processes and 

Criteria Nonattainment .201 

Addressing Natural Exceedance of the Chesapeake Bay Criteria .. .201 

Natural excursions of low dissolved oxygen conditions.202 

Natural reductions in water clarity levels .206 

Natural elevated chlorophyll a concentrations.209 





























Diagnosing Causes of Criteria Nonattainment.210 

Dissolved oxygen criteria.210 

Water clarity criteria.211 

Chlorophyll a criteria.218 

Literature Cited.218 

Glossary .221 








chapte r\/l 

Recommended 
Implementation Procedures 


This chapter presents implementation procedures as regional guidance to the Chesa¬ 
peake Bay watershed states and other agencies, institutions, groups or individuals 
applying the criteria to determine the degree of attainment. In accordance with 
Section 117(b)(2)(B)(iii) of the Clean Water Act, these procedures accompany the 
regional criteria to promote their consistent, baywide application in common tidal- 
water designated uses across jurisdictional boundaries. 

The Chesapeake Bay criteria, as presented in the previous three chapters, will protect 
designated uses if they are applied strictly following current EPA national guide¬ 
lines. The regional implementation procedures described in this chapter are tailored 
to the Chesapeake Bay and its tidal tributaries, the refined tidal-water designated 
uses and the current and anticipated enhancements to the baywide coordinated moni¬ 
toring program. Adoption and application of the Chesapeake Bay-specific 
implementation procedures across jurisdictions will give the states and other partners 
a greater degree of confidence in assessing the attainment of criteria and protection 
of designated uses. The extensive shared tidal waters should be assessed consistently 
across the four jurisdictions using these recommended procedures that account for 
natural conditions and processes, highlight the magnitude and extent of remaining 
impairments and provide up-front diagnostics of possible reasons for criteria nonat¬ 
tainment. The EPA strongly encourages states to adopt these implementation 
procedures into their water quality standards. 

The chapter includes: 

• A brief review of the criteria, defining the spatial and temporal boundaries 
within which criteria attainment will be measured; 

• A method for quantifying and visualizing the degree of criteria attainment or 
exceedance that incorporates the amount of area or volume of a region that 
meets or exceeds a criterion and how often a criterion is met or exceeded; 

• A description of successful criteria attainment recognizing that 100 percent 
attainment is not necessary to protect designated and existing uses; 


chapter vi • Recommended implementation Procedures 



• A practical description of how monitoring information may be used to assess 
attainment, including statistical estimation methods for addressing assessment 
of the short-interval criteria, such as the 7-day mean, 1-day mean and instanta¬ 
neous minimum dissolved oxygen criteria; and 

• A description of how mathematical model-simulated information may be used 
to assess the effect on future criteria attainment under various nutrient/sediment 
reduction scenarios, which support decisions on load reductions and caps on 
loadings to maximize the beneficial effect on attainment. 


DEFINING CRITERIA ATTAINMENT 

DISSOLVED OXYGEN CRITERIA 

The Chesapeake Bay dissolved oxygen criteria were derived to protect species and 
communities in the five tidal-water designated uses during specific seasons (Table 
VI-1). See Chapter III for detailed information on the designated use-specific criteria 
and appropriate periods for applying them. Refer to Appendix A and the Technical 
Support Document for the Identification of Chesapeake Bay Designated Uses and 
Attainability (U.S. EPA 2003) for details on the five designated uses and their bound¬ 
aries. The Chesapeake Bay dissolved oxygen criteria should not be applied to a 
designated use or during a period of the year for which they were not specifically 
derived (see Chapter III). 

The EPA expects the states to adopt the full set of dissolved oxygen criteria that will 
protect the refined tidal-water designated uses, presented in Table VI-1. Given recog¬ 
nized limitations in direct monitoring at the temporal scales required for assessing 
attainment of the instantaneous minimum, 1-day mean and 7-day mean criteria (see 
section titled “Monitoring to Support the Assessment of Criteria Attainment” for 
more details), states can waive attainment assessments for these criteria until moni¬ 
toring at the required temporal scales is implemented or apply statistical methods to 
estimate probable attainment. Where sufficient data at these temporal scales exist for 
specific regions or local habitats, states should assess attainment of the full set of 
applicable dissolved oxygen criteria. 

WATER CLARITY CRITERIA 

The Chesapeake Bay water clarity criteria were derived based on the minimum 
percent light-through-water (PLW) requirements of underwater bay grasses (Table 
VI-2). These criteria apply only to shallow-water designated use habitats. The water 
clarity criteria are not intended to apply in areas where underwater bay grasses are 
precluded from growing by non-water clarity-related factors such as excessive wave 
action or at depths where natural and other physical habitat factors will prevent 
sufficient light penetration required by the plants. See Chapter IV for a discussion of 
the salinity regime-specific criteria and time periods for application. Refer to 


chapter vi 


Recommended Implementation Procedures 


( 



147 





chapter vi • Recommended Implementation Procedures 


1 At temperatures considered stressful to shortnose sturgeon (>29°C), dissolved oxygen concentrations above an instantaneous minimum of 4.3 mg 
liter" 1 will protect survival of this listed sturgeon species. 


























Table VI-2. Summary of Chesapeake Bay water clarity criteria for application to shallow-water 
bay grass designated use habitats. 


Salinity 

Regime 

Water Clarity 
Criteria as 
Percent Light- 
through-Water 

Water Clarity Criteria as Secchi Depth 

Temporal 

Application 

Water Clarity Criteria Application Depths 

0.25 

0.5 

0.75 

1.0 

1.25 

1.5 

1.75 

2.0 

Secchi Depth (meters) for above Criteria Application Depth 

Tidal-fresh 

13 % 

0.2 

0.4 

0.5 

0.7 

0.9 

1.1 

1.2 

1.4 

April 1 - October 31 

Oligohaline 

13% 

0.2 

0.4 

0.5 

0.7 

0.9 

1.1 

1.2 

1.4 

April 1 - October 31 

Mesohaline 

22% 

0.2 

0.5 

0.7 

1.0 

1.2 

1.4 

1.7 

1.9 

April 1 - October 31 

Polyhaline 

22% 

0.2 

0.5 

0.7 

1.0 

1.2 

1.4 

1.7 

1.9 

March 1 - May 31, 
September 1 - November 30 


'Based on application of Equation IV-1, PLW = 100exp(-K d Z), the appropriate PLW criterion value and the selected application 
depth are inserted and the equation is solved for Kj. The generated Kj value is then converted to Secchi depth (in meters) using 
the conversion factor = 1,45/Secchi depth. 


Appendix A and U.S. EPA (2003) for broad and detailed descriptions, respectively, 
of the shallow-water designated use and its boundaries. 

The Chesapeake Bay water clarity criteria should not be applied to a designated use 
or in a period during the year for which they were not derived. The March 1 through 
May 31 and September 1 through November 30 temporal application for the poly¬ 
haline water clarity criteria was originally established for protection of eelgrass 
(Zostera marina) beds (Batiuk et al. 1992). Widgeon grass (Ruppia maritima) co¬ 
occurs with eelgrass in polyhaline habitats. In shallow-water habitats where both 
species currently or historically co-occur 1 , states and other users should assess water 
clarity criteria attainment using a March 1 through November 30 or April 1 through 
October 31 temporal application period. 

When the water clarity criteria were derived, there was an insufficient scientific basis 
for deriving a set of water clarity or related (e.g., total suspended solids) criteria for 
protection of open-water designated use habitats. 

The EPA expects the states to adopt the salinity regime-specific water clarity criteria 
to protect their shallow-water designated uses, presented in Table VI-2. States are 
expected to measure the achievement of the shallow-water designated use at the 
Chesapeake Bay Program segment scale by achieving an established acreage of 
underwater bay grasses, attainment of the applicable water clarity criteria at an 


'Maps of the potential and recent distributions of both species were published by Batiuk et al. (1992); 
see page 125 for eelgrass and page 128 for widgeon grass. Further information on underwater bay grass 
aerial survey findings on the distribution of these two species can also be found at the Virginia Institute 
of Marine Science’s website at http://www.vims.edu/bio/sav. 


chapter vi 


Recommended Implementation Procedures 






























established application depth or attainment of the applicable water clarity criteria 
throughout an established potential shallow-water habitat acreage. The available 
supporting technical information on segment-specific underwater bay grass 
acreages, application depths and potential shallow-water habitat acreages are 
described in the “Monitoring to Support the Assessment of Criteria Attainment,” 
section of this chapter and published in detail in the Technical Support Document for 
the Identification of Chesapeake Bay Designated Uses and Attainability (U.S. EPA 
2003). 

CHLOROPHYLL A CRITERIA 

Because of the regional and site-specific nature of algal-related water quality impair¬ 
ments, only narrative chlorophyll a criteria have been published here. The 
chlorophyll a concentrations tabulated in Chapter V are not numerical EPA criteria. 
Along with the documented methodologies, they are provided as a synthesis of the 
best available technical information supporting the states’ development and adoption 
of site-specific numerical chlorophyll a criteria or the derivation of numerical trans¬ 
lators for their narrative chlorophyll a criteria. 

The narrative Chesapeake Bay chlorophyll a criteria were derived to address the full 
array of possible impairments, all of which may not manifest themselves within a 
particular water body at a given time (Table VI-3). The site-specific nature of impair¬ 
ments caused by the overabundance of algal biomass supports the states’ adoption of 
the EPA-recommended narrative criteria, with application of site-specific numeric 
criteria only for localized waters addressing local algal-related impairments. 

The EPA expects states to adopt narrative chlorophyll a criteria into their water 
quality standards for all Chesapeake Bay and tidal tributary waters. The EPA 
strongly encourages states to develop and adopt site-specific numerical chlorophyll 
a criteria for tidal waters where algal-related impairments persist after the Chesa¬ 
peake Bay dissolved oxygen and water clarity criteria have been attained. 

The formulation and ultimately the assessment of numerical chlorophyll a criteria 
should be based upon seasonal dynamics and concentrations of chlorophyll a in the 
Chesapeake Bay and its tributaries. Spring and summer were chosen for these 
purposes. Any site-specific numerical impairment-based chlorophyll a criteria 
should be applied as salinity regime-based spring (March through May) and summer 
(July through September) seasonal mean concentrations. 

Table VI-3. Recommended Chesapeake Bay chlorophyll a narrative criteria. 

Concentrations of chlorophyll a in free-floating microscopic aquatic plants (algae) shall 
not exceed levels that result in ecologically undesirable consequences-such as reduced 
water clarity, low dissolved oxygen, food supply imbalances, proliferation of species 
deemed potentially harmful to aquatic life or humans or aesthetically objectionable 
conditions-or otherwise render tidal waters unsuitable for designated uses. 


I 


chapter vi • Recommended Implementation Procedures 





ADDRESSING MAGNITUDE, DURATION, 
FREQUENCY, SPACE AND TIME 


( 



To define and measure criteria attainment, a number of factors are taken into 
account. According to a recent National Research Council (2001) review, estab¬ 
lishing the “magnitude, duration and frequency” of a condition is crucial for 
successful development and application of state water quality standards. Equally 
important is the spatial extent of a condition, and the spatial and temporal dimen¬ 
sions of attainment assessment must be defined. 

Magnitude refers to how much of the pollutant—or a given quantifiable measure of 
condition—can be allowed while still achieving the designated uses. Magnitude is 
assessed through a direct comparison of ambient concentrations with the appropriate 
Chesapeake Bay criterion value. The magnitude of nonattainment of a criterion value 
also provides information useful to making management decisions on taking correc¬ 
tive actions. 

Attainment of all three Chesapeake Bay criteria should be assessed by Chesapeake 
Bay segment (Figure VI-1; Table VI-4), separately for each designated use habitat. 
Therefore, each designated use habitat in an individual Chesapeake Bay Program 
segment is considered a spatial assessment unit. This is consistent with the scale of 
data aggregation and reporting for Chesapeake Bay tidal-water quality monitoring 
and the physical scale of the designated use areas. 

Criteria attainment should be presented in terms of spatial extent, i.e., the percentage 
of the volume (dissolved oxygen) or surface area (water clarity, chlorophyll a) of the 
particular designated use habitat in each Chesapeake Bay Program segment that 
meets or exceeds the applicable criteria. Measuring spatial extent will be enabled 
through the use of spatial interpolation methods, which are described later in this 
chapter. Such ‘interpolators’ work by dividing a water body into a three-dimensional 
grid, with cell size depending on data density and the application’s resolution 
requirements, among other factors. 

Duration is defined as the period over which exposure to the constituent of concern 
is to be averaged within the assessment period (see below) to prevent detrimental 
effects. Duration can also be thought of as the allowable time of exposure before 
effects occur. For example, the open-water dissolved oxygen criteria includes a crite¬ 
rion with a magnitude of 5 mg liter" 1 evaluated as a 30-day mean; another criterion 
has a magnitude of 4 mg liter" 1 evaluated as a 7-day mean. 

The dissolved oxygen, water clarity and chlorophyll a criteria are season-specific, 
and attainment should be measured only over the applicable season. For example, 
attainment of the dissolved oxygen criteria for the migratory fish spawning and 
nursery designated use should be assessed and reported for the period of February 1 
through May 31; attainment of the open-water fish and shellfish designated use 


chapter vi 


Recommended Implementation Procedures 




criteria, as applied to both open- and shallow-water bay grass designated uses, 
should be assessed and reported seasonally, in winter (December, January and 
February), spring (March, April and May), summer (June, July, August and 
September) and fall (October and November). Tables VI-1 and VI-2 define ‘seasons’ 
and applicable criteria for dissolved oxygen and water clarity, respectively. Numer¬ 
ical chlorophyll a criteria should be applied to the spring and summer seasons 
defined previously. 

The assessment period refers to the most recent three consecutive years for which 
relevant monitoring data are available. In circumstances where three consecutive 
years of data are not available, a minimum of three years within the most recent five 
years should be used. 

A three-year period is consistent with the water quality status assessment period 
used for over a decade by the Chesapeake Bay Program partners (e.g., Alden and 
Perry 1997). A three-year period includes some natural year-to-year variability 
largely due to climatic events, and it also addresses residual effects of one year’s 
conditions on succeeding years. Two years is not enough time to assess central 
tendency, and four or more years delay response to problems that may be detected. 
Longer periods are more appropriate for detecting trends than for characterizing 
current water quality conditions. 

A comparison of criteria attainment across one-, three- and five-year assessment 
periods confirmed the selection of three years as the appropriate temporal averaging 
period. Attainment levels were highly variable using single-year periods. The five- 
year period smoothed much of the variability and resulted in little difference 
between one assessment period and the next. 

The allowable frequency at which the criterion can be violated without a loss of the 
designated use also must be considered. Frequency is directly addressed through 
comparison of the generated cumulative frequency distribution with the applicable 
criterion reference curve. All values falling below the reference curve are considered 
biologically acceptable exceedances of the applicable Bay criteria. Through its deri¬ 
vation, the reference curve directly incorporates a biologically acceptable frequency 
of exceedances of the applicable Chesapeake Bay criteria. 

By combining these factors to measure attainment, the spatial extent of violation or 
attainment of the criterion can be determined for each designated use within each 
Chesapeake Bay Program segment at temporal increments defined by the criterion. 
As the next section describes, the frequency of these occurrences is tallied for each 
season over the assessment period. 


chapter vi • Recommended Implementation Procedures 


NORTF 


CB1TE 
BSHOH-i 

GUNOH— 

MIDOH. 

BACOH' 


ELKOH 

CADOH 

BOHOH 

SASOH 


ANATF 


POTOH 


NANTF 

NANOH 

NANMH 


JMSTF 


APPTF 



POCTF 


POCOH 


POCMH 


MOBPH 


CB8PH 


LYNPH 


ELIPH 
WBFMH — 1 


■—LAFMH 
‘-FBEMH 


SBEMH 


Figure VI-1. The geographical location of the 78 Chesapeake Bay Program segments. 
Source: Chesapeake Bay Program 1999. 


hapter vi • Recommended Implementation Procedures 

























Table VI-4. Chesapeake Bay Program segmentation scheme segments. 


Northern Chesapeake Bay.CB1TF 

Upper Chesapeake Bay.CB20H 


Upper Central Chesapeake Bay .... CB3MH 
Middle Central Chesapeake Bay ... CB4MH 
Lower Central Chesapeake Bay .... CB5MH 
Western Lower Chesapeake Bay .... CB6PH 


Eastern Lower Chesapeake Bay .... CB7PH 

Mouth of Chesapeake Bay .CB8PH 

Bush River .BSHOH 

Gunpowder River .GUNOH 

Middle River.MIDOH 

Back River.BACOH 

Patapsco River .PATMH 

Magothy River.MAGMH 

Severn River.SEVMH 

South River.SOUMH 

Rhode River .RHDMH 

West River.WSTMH 

Upper Patuxent River .PAXTF 

Western Branch Patuxent River .... WBRTF 

Middle Patuxent River.PAXOFI 

Lower Patuxent River .PAXMH 

Upper Potomac River .POTTF 

Anacostia River.ANATF 

Piscataway Creek .PISTF 

Mattawoman Creek.MATTF 

Middle Potomac .POTOH 

Lower Potomac .POTMH 

Upper Rappahannock River.RPPTF 

Middle Rappahannock River .RPPOH 

Lower Rappahannock River.RPPMH 

Corrotoman River.CRRMH 

Piankatank River.PIAMH 

Upper Mattaponi River.MPNTF 

Lower Mattaponi River.MPNOH 

Upper Pamunkey River .PMKTF 

Lower Pamunkey River.PMKOH 

Middle York River.YRKMH 

Lower York River.YRKPH 


Mobjack Bay.MOBPH 

Upper James River.JMSTF 

Appomattox River .APPTF 

Middle James River .JMSOH 

Chickahominy River .CHKOH 

Lower James River.JMSMH 

Mouth of the James River.JMSPH 


Western Branch Elizabeth River ... WBEMH 
Southern Branch Elizabeth River ... SBEMH 


Eastern Branch Elizabeth River .... EBEMH 


Lafayette River.LAFMH 

Mouth to mid-Elizabeth River.ELIPH 

Lynnhaven River.LYNPH 

Northeast River.NORTF 

C&D Canal .C&DOH 

Bohemia River.BOHOH 

Elk River.ELKOH 

Sassafras River.SASOH 

Upper Chester River.CHSTF 

Middle Chester River.CHSOH 

Lower Chester River.CHSMH 

Eastern Bay .EASMH 

Upper Choptank River .CHOTF 

Middle Choptank River.CHOOH 

Lower Choptank River.CHOMH1 


Mouth of the Choptank River .... CHOMH2 


Little Choptank River .LCHMH 

Honga River.HNGMH 

Fishing Bay .FSBMH 

Upper Nanticoke River.NANTF 

Middle Nanticoke River .NANOH 

Lower Nanticoke River.NANMH 

Wicomico River.WICMH 

Manokin River.MANMH 

Big Annemessex River.BIGMH 

Upper Pocomoke River.POCTF 

Middle Pocomoke River.POCOH 

Lower Pocomoke River.POCMH 

Tangier Sound .TANMH 


Source: Chesapeake Bay Program 1999. 


chapter vi • Recommended Implementation Procedures 








































































DEVELOPING THE CUMULATIVE 
FREQUENCY DISTRIBUTION 


( 



The use of cumulative frequency distributions (CFDs) is recommended for assessing 
spatial and temporal water quality criteria exceedance in the Chesapeake Bay. CFDs 
offer a number of advantages over other techniques that are applied for this purpose. 
First, the use of CFDs is well established in both statistics and hydrologic science. 
CFDs have been used for much of the past century to describe variations in hydro- 
logic assessments (Haan 1977). For example, the U.S. Geological Survey has 
traditionally used CFDs to describe patterns in historical streamflow data for the 
purpose of evaluating the potential for floods or droughts (Helsel and Hirsch 1992). 

Second, the application of the CFD for evaluating water quality criteria attainment 
in the Chesapeake Bay allows for the evaluation of both spatial and temporal varia¬ 
tions in criteria exceedance. Methods currently used for the assessment of criteria 
attainment are based only on temporal variations because measurements are usually 
evaluated only at individual monitoring station locations. One of the limitations of 
this approach is that it is often difficult to determine whether an individual sampling 
location is representative, and there is always potential for bias. In a water body the 
size of the Chesapeake Bay, accounting for spatial variation can be very important 
and in that respect, the CFD approach represents a significant improvement over 
methods used in the past. 

A CFD is developed first by quantifying the spatial extent of criteria exceedance for 
every monitoring event during the assessment period. Compiling estimates of spatial 
exceedance through time accounts for both spatial and temporal variation in criteria 
exceedance. Assessments are performed within spatial units defined by the intersec¬ 
tion of Chesapeake Bay Program segments (see Figure VI-1) and the refined 
tidal-water designated uses (see U.S. EPA 2003 for specific boundaries), and 
temporal units of three-year periods. Thus, individual CFDs will be developed for 
each spatial assessment unit over three-year assessment periods. Details on the steps 
involved in developing CFDs are described below. 

STEP 1. INTERPOLATION OF WATER QUALITY MONITORING DATA 

The Chesapeake Bay Program partners collect monitoring data over a range of spatial 
scales and frequencies. Much of the water quality monitoring data collected in the 
Chesapeake Bay and its tidal tributaries is drawn from a limited number of fixed 
stations that are visited on a monthly (or more frequent) basis. Other types of data are 
collected at different spatial frequencies. For example, some chlorophyll a data are 
collected in a spatially continuous in-situ manner along the cruise tracks of moni¬ 
toring vessels. All of the different types of data are useful for assessing criteria 
attainment; however, they must be connected to a single spatial framework in order to 
provide a common basis for interpretation. Assessment of criteria attainment requires 
that conclusions be drawn for all locations within a spatial unit and not just the loca- 


chapter vi 


Recommended Implementation Procedures 


( 



tions where data may have been collected. Thus, the data must be extrapolated in 
order to evaluate criteria attainment for the larger spatial unit that the data represent. 

For the Chesapeake Bay and its tidal tributaries, using a grid-based spatial interpola¬ 
tion software provides a common spatial framework and spatial extrapolation. Spatial 
interpolation provides estimates of water-quality measures for all locations within a 
spatial assessment unit. This is accomplished at any single location by linear interpo¬ 
lation of the data of all its nearest neighbors. This approach provides an estimate of 
the water quality measure at all locations within the spatial unit being considered. 

An example of the use of spatial interpolation is illustrated in Figure VI-2, which 
displays the monitoring segment boundaries and fixed-station locations in the area 



Figure VI-2. Chesapeake Bay Program segment boundaries, fixed monitoring station 
locations and summer chlorophyll a concentration (pg liter 1 ) distribution in the Tangier 


Sound area of the Eastern Shore of Maryland and Virginia. Summer chlorophyll a 


concentration distribution is defined by spatial interpolation. 


chapter vi • Recommended Implementation Procedures 









around Tangier Sound and the adjacent portion of the Eastern Shore of Maryland and 
Virginia. Using spatial interpolation, chlorophyll a concentrations were estimated 
for all locations in the Tangier Sound area. Based on those estimates, the spatial 
distribution of chlorophyll a is illustrated by shading the area according to the esti¬ 
mated concentration (darker shading represents higher chlorophyll a 
concentrations). The results illustrate the spatial gradients that tend to occur 
throughout an area of this size. Those gradients need to be accounted for in order to 
accurately assess the extent of criteria exceedance. 

The Chesapeake Bay Program spatial-interpolation software (or ‘CBP interpolator’) 
computes water quality concentrations throughout the Chesapeake Bay and its tidal 
tributaries from measurements collected at point locations or along cruise tracks 
(Bahner 2001). It estimates water quality concentrations at all locations in a two- 
dimensional area or in a three-dimensional volume. The CBP interpolator is 
cell-based. Fixed cell locations are computed by interpolating the nearest number ( n ) 
of neighboring water quality measurements, where n is normally 4, but is adjustable. 
Typically an interpolation is performed for the entire Chesapeake Bay for a single 
monitoring event (e.g., a monthly cruise). In this way all monitoring stations are used 
to develop a bay wide picture of the spatial variation of the parameter being consid¬ 
ered. Segment and designated use boundaries can then be superimposed over the 
baywide interpolation to assess the spatial variation of the parameter in any one 
segment’s designated use(s). 

Cell size in the Chesapeake Bay was chosen to be 1 kilometer (east-west) by 1 kilo¬ 
meter (north-south) by 1 vertical meter, with columns of cells extending from the 
surface to the bottom of the water column, thus representing the three-dimensional 
volume as a group of equal-sized cells. The tidal tributaries are represented by 
various cell sizes, depending on the geometry of the tributary, since the narrow 
upstream portions of the tidal rivers require smaller cells to represent the river’s 
dimensions accurately. This configuration results in a total of 51,839 cells for the 
mainstem Chesapeake Bay and a total of 238,669 cells for the Chesapeake Bay and 
its tidal tributaries. 

The CBP interpolator is tailored for use in the Chesapeake Bay in that the code is 
optimized to compute concentration values that closely reflect the physics of strati¬ 
fication. The Chesapeake Bay is very shallow despite its width and length; hence 
water quality varies much more vertically than horizontally. The CBP interpolator 
uses a vertical filter to select the vertical range of data for each calculation. For 
instance, to compute a model cell value at 5-meters deep, monitoring data at 5 meters 
are preferred. If fewer than n (4) monitoring data values are found at the preferred 
depth, the depth window is widened to search up to d (normally ±2m) meters above 
and below the preferred depth, with the window being widened in 0.5-meter incre¬ 
ments until n monitoring values have been found for the computation. The user is 
able to select the smallest n value that is acceptable. If fewer than n values are 
located, a missing value (normally a -9) is calculated for that cell. 


chapter vi 


Recommended Implementation Procedures 




A second search radius filter is used to limit the horizontal distance of monitoring data 
from the cell being computed. Data points outside the radius selected by the user 
(normally 25,000 meters) are excluded from calculation. This filter is included so that 
only data near a specific location are used for interpolation. In the current version of 
the CBP interpolator, segment and region filters have been added (Bahner 2001). 

The Chesapeake Bay Program segments are geographic limits for interpolation. For 
instance, the mainstem Chesapeake Bay is composed of eight segments (see Figure 
VI-1 and Table VI-4). The tidal tributaries are composed of 70 additional segments, 
using the Chesapeake Bay Program 1998 segmentation scheme (CBP 1999). Each 
segment represents a geographic area that has somewhat homogeneous environ¬ 
mental conditions. Segmentation enables users to report findings on a 
segment-by-segment basis, which can reveal localized changes compared to the 
entire Chesapeake Bay ecosystem. 

As stated above, the CBP interpolator uses monitoring data to fill in the three-dimen¬ 
sional space of the Chesapeake Bay. The CBP interpolator assumes a linear 
distribution of the data between points. Given the dynamic nature of estuaries, this 
is obviously a conservative assumption. However, the spatial limitations of the data 
make the simplest approach the most prudent. The strength of the CBP interpolator’s 
output is directly related to the quality and spatial resolution of the input data. As 
sample size increases, interpolation error decreases. For more detailed documenta¬ 
tion on the Chesapeake Bay Program interpolator and access to a downloadable 
version, refer to the Chesapeake Bay Program web site at http://www. 
chesapeakebay.net/tools.htm. 

STEP 2. COMPARISON OF INTERPOLATED WATER QUALITY 
MONITORING DATA TO THE APPROPRIATE CRITERION VALUE 

To quantify the spatial extent of criteria exceedance, the interpolated water quality 
monitoring data must be compared to the appropriate criteria value. In all cases, the 
water quality criteria are defined within specific spatial limits and with varying spatial 
values. In order to define the spatial extent of criteria exceedance, the appropriate 
criteria values must be aligned with the water quality measures throughout the spatial 
assessment unit. Accordingly, the spatial definition of each criterion is superimposed 
on the interpolator grid structure to assign a criteria value to each cell. Criteria 
assessments can then be made on a cell-by-cell basis using the water quality estimate 
from the interpolator and the criteria value defined for each cell. Figure VI-3 illus¬ 
trates a schematic of the process for spatially defined criteria assessment. Chlorophyll 
a estimates generated from the interpolator (such as that for Tangier Sound, Figure 
VI-2) are combined with the grid-based definition of criteria values. The integration 
of those two layers allows the comparison of ‘measured’ chlorophyll a to the appli¬ 
cable criteria value in each cell to determine if that cell exceeds the criterion for the 
time period for which data were collected (Figure VI-3). 


chapter vi • Recommended Implementation Procedures 



SEGMENT 



Figure VI-3. Chlorophyll a concentration values estimated for each interpolator cell 
are compared to the appropriate criterion value on a cell-by-cell basis to determine the 
spatial extent of exceedance. 


STEP 3. IDENTIFICATION OF INTERPOLATOR CELLS 
THAT EXCEED THE CRITERION VALUE 

When the appropriate criterion value has been assigned to each interpolator cell, 
comparisons can be made on a cell-by-cell basis to determine if the estimated water 
quality values met or exceeded the criteria at the time of the monitoring event. Eval¬ 
uation of criteria exceedance is performed for each cell in a spatial unit (Figure 
VI-4a), enabling the entire spatial unit to be characterized. The percentage of cells 
that exceed the criteria represents the spatial extent of exceedance in that spatial unit 
and for that sampling event. The same process is repeated for every sampling event 
(Figure VI-4b) and the compilation of the estimates of the extent of spatial 
exceedance provides an indication of the frequency of exceedance. 

STEP 4. CALCULATION OF THE CUMULATIVE PROBABILITY 
OF EACH SPATIAL EXTENT OF EXCEEDANCE 

The spatial extent of exceedance (represented by the colored cells in Figure VI-4) is 
calculated as the percentage of area or volume exceeding the criteria. This is accom¬ 
plished by simply dividing the area or volume of all the cells exceeding the criteria 
by the total area or volume of the spatial assessment unit and multiplying by 100. 


chapter vi 


Recommended Implementation Procedures 






a. 


b. 



Month 

Percent Area/ 

Volume 

Month 

Percent Area/ 
Volume 

Rank 


March 1998 

72 

April 1998 

55 

May 1998 

65 

June 1998 

75 

March 1999 

49 

April 1999 

34 

May 1999 

67 

June 1999 

25 

March 2000 

20 

April 2000 

39 

May 2000 

35 

June 2000 

50 


June 1998 

75 

1 

March 1998 

72 

2 

May 1999 

67 

3 

May 1998 

65 

4 

April 1998 

55 

5 

June 2000 

50 

6 

March 1999 

49 

7 

April 2000 

39 

8 

May 2000 

35 

9 

April 1999 

34 

10 

June 1999 

25 

11 

March 2000 

20 

12 


Figure VI-5. To develop a CFD for an area/volume, estimates of spatial extent of criteria exceedance for all of the 
sampling events conducted over a three-year assessment period (See Figure Vl-4b) are compiled (a). To prepare for 
developing the CFD the estimates of spatial extend of exceedance are sorted in descending order (b) and ranked. 


a. b. 

Percent Area/ Percent Area/ Cumulative Probability 

Month Volume Rank Month Volume Rank [Rank/(n+1)] 


June 1998 

75 

1 

March 1998 

72 

2 

May 1999 

67 

3 

May 1998 

65 

4 

April 1998 

55 

5 

June 2000 

50 

6 

March 1999 

49 

7 

April 2000 

39 

8 

May 2000 

35 

9 

April 1999 

34 

10 

June 1999 

25 

11 

March 2000 

20 

12 



100 


0.00% 

June 1998 

75 

1 

7.69% 

March 1998 

72 

2 

15.38% 

May 1999 

67 

3 

23.08% 

May 1998 

65 

4 

30.77% 

April 1998 

55 

5 

38.46% 

June 2000 

50 

6 

46.15% 

March 1999 

49 

7 

53.85% 

April 2000 

39 

8 

61.54% 

May 2000 

35 

9 

69.23% 

April 1999 

34 

10 

76.92% 

June 1999 

25 

11 

84.62% 

March 2000 

20 

12 

92.31% 


0 


100.00% 


Figure VI-6. To develop a CFD, estimates of spatial extent of criteria exceedance for all of the sampling events 
conducted over a three-year assessment period (see Figure VI-4) are compiled, sorted in descending order and 
ranked (a). Cumulative probability is calculated using the formula ’rank/(n + 1)' (b). 


chapter vi 


Recommended Implementation Procedures 














































































STEP 5. PLOT OF SPATIAL EXCEEDANCE VS. 
THE CUMULATIVE FREQUENCY 


The CFD is a graphical illustration that summarizes criteria exceedance by plotting 
the temporal and spatial exceedance values listed in Figure VI-6. Temporal 
frequency of exceedance is plotted on the vertical axis and spatial extent of 
exceedance on the horizontal axis (Figure VI-7). The resulting figure can be used to 
draw conclusions about the extent and pattern of criteria exceedance. Each point on 
the curve represents the cumulative amount of space and time in which the criteria 
were exceeded. The potential for observing a spatial extent of exceedance greater 
than the one observed is indicated by the temporal frequency. The curve in Figure 
VI-7 shows two examples of the interpretations of individual points. In addition to 
the interpretation of individual point, the area beneath the curve represents a spatial 
and temporal composite index of criteria exceedance. This area is recommended as 
the basis for defining criteria attainment for all Chesapeake Bay segments and desig¬ 
nated uses. 





co 

CD 


E 






Wm -v,llillfe&il MiMcm &x ’' ?S fen llUl Wsm 


Figure VI-7. The horizontal axis is the spatial extent of criteria exceedance based on monitoring data 
extrapolated using spatial interpolation. The vertical axis is the cumulative frequency of criteria exceedance 
for the monitoring events conducted during the assessment period. 


chapter vi • Recommended implementation Procedures 



















The shape of the curve also indicates the spatial and temporal pattern of criteria 
exceedance. Figure VI-8 illustrates three potentially observable CFD plots. Curve (a) 
indicates a situation in which the water quality criteria are chronically exceeded in a 
relatively small amount of a given segment. Managers could use this information to 
target segments for further monitoring and assessment and to identify chronic prob¬ 
lems and tailor management plans to address them. Curve (b) illustrates a situation 
where criteria are exceeded on a broad spatial scale, but relatively infrequently. Such 
broad-scale acute problems should be evaluated individually. If the frequency and 
duration of broad-scale criteria exceedances were low enough, ecological impacts 
could be limited. On the other hand, some short-term exceedances can have signifi¬ 
cant ecological effects. Curves (a) and (b) reflect a similar degree of overall criteria 
exceedance; however, the exceedance of curve (a) is primarily temporal, and the 
exceedance of curve (b) is primarily spatial. Curve (c) reflects broad-scale criteria 
exceedance in both space and time. The shape of the curves should be used for diag¬ 
nostic purposes only. Decisions regarding full attainment should be based on the 
overall amount of criteria exceedance indicated by the area under the curve. 

As discussed above, it is possible that some spatial and temporal criteria exceedances 
could be observed, without necessarily having significant effects on ecological 
health or on the designated use of a portion of the Chesapeake Bay. Such 
exceedances are referred to as ‘allowable exceedances.’ Such exceedances have been 



Figure VI-8. Use of cumulative frequency distribution to characterize patterns of water 
quality criteria exceedance. Curve (a) indicates that criteria are chronically exceeded in a 
relatively small portion of the spatial unit. Curve (b) indicates that criteria are exceeded 
over a large portion of the spatial unit on a relatively infrequent basis. Curve (c) indicates 
that criteria are exceeded over large portions of space and time. 


chapter vi 


Recommended Implementation Procedures 







provided for in EPA national guidance for assessing criteria attainment (U.S. EPA 
1997). Ten percent of the samples collected at a point are allowed to reflect nonat¬ 
tainment of water quality criteria without indicating nonattainment of designated 
uses. These criteria exceedances are considered ‘allowable exceedances’ that had 
limited impact on the designated use. The 10-percent rule is not directly applicable 
in the context of the CFD methodology for defining criteria attainment because it 
was designed for samples collected at one location and, therefore, is only reflective 
of time. 

A more appropriate approach for defining ‘allowable exceedances’ in the CFD 
context is to develop a reference curve (described below) that identifies the amount of 
spatial and temporal criteria exceedance that can occur without causing significant 
ecological degradation. Such curves can be based on biological indicators of ecolog¬ 
ical health that are separate from the criteria measures themselves. Biological 
indicators can be used to identify areas of the Chesapeake Bay and its tidal tributaries 
that have healthy ecological conditions and supportive water quality conditions. CFDs 
can be developed for those areas as well. Since healthy ecological conditions exist in 
the selected areas, CFDs developed for the area would reflect an extent and pattern of 
criteria exceedance that did not have significant ecological impact. Thus, the refer¬ 
ence curve approach takes the development of criteria levels beyond those developed 
in a laboratory setting and provides actual environmental context. Small incidents of 
spatial and temporal criteria exceedance that do not have ecological impacts are iden¬ 
tified and allowed in the assessment of criteria attainment. A description of the 
application of the reference curve is provided in this section, with more details on 
reference curves in the section titled “Defining the Reference Curve.” 

Figure VI-9 illustrates the use of the reference curve and the interpretation of criteria 
attainment using the CFD. The light blue line illustrates a possible reference curve, 
below which a certain amount of spatial or temporal exceedance is allowed. An 
actual reference curve could be asymmetrical, indicating that the system could with¬ 
stand either short-term excursions in time or chronic exceedances in small portions 
of space, but not both. 

Development of the reference curve is intended to identify such specifics to more 
accurately reflect what the ecological system needs to thrive. It also is intended to be 
developed as a benchmark that is not changed on a regular basis, recognizing the 
potential for updates as new information is gathered. By contrast, the attainment 
curve is developed over every assessment period during which monitoring data are 
collected. 

The attainment curve is the assessment of the condition in the segment during the 
assessment period and is compared to the reference curve. The area above the refer¬ 
ence curve and below the attainment curve reflects criteria attainment and is referred 
to as “non-allowable exceedances.” It is recommended that separate attainment 
curves be developed for each criteria component, for subsequent application in every 
spatial assessment unit (Chesapeake Bay Program segment/designated use) and for 
at least one full assessment period of three years. 


chapter vi • Recommended Implementation Procedures 




Figure VI-9. Light area reflects amount of 'allowable' criteria exceedance defined as 
the area under the reference curve (light line). Dark area reflects the amount of 'non¬ 
allowable' criteria exceedance defined as the area between the attainment 
curve (black line) and the reference curve. 


In cases where the amount of ‘non-allowable exceedances’ is large (e.g., Figure VI- 
8, line c; Figure VI-9), decisions regarding the attainment of designated uses will be 
unequivocal. However, situations could arise where small amounts of non-allowable 
exceedance could render the decisions less clear. Figure VI-10 illustrates a situation 
in which a decision on nonattainment might be clear (a) and one in which the deci¬ 
sion might be less clear (b). In the latter case, questions could arise about the 
certainty of the analysis and whether the data were adequate to unequivocally decide 
that the portion of the Chesapeake Bay was not attaining its designated use. In some 
cases, many data points could have contributed to the development of the CFD, 
whereas in other cases there may have been only a few. It is possible to define the 
decision rule that any non-allowable exceedance would indicate nonattainment of 
the established designated use. However, a decision rule based on a statistical test 
could help to address some of the uncertainty involved by accounting for differences 
in the number of observations on which the analysis is based. 

Work is currently under way to devise a statistical test for the application of CFDs 
to assess water quality criteria attainment in the Chesapeake Bay. The test currently 


chapter vi • Recommended Implementation Procedures 









( 


0) TO 

SB 

© 6 
E © 

© jt 
CL *- 
_ </) 
^ "o 
© © 
s © 
o o 

® X 
CL LU 

w © 

ro E 

© 3 

E o 


o © 
© < 
ro o 
c © 
8 o 
© 5 

o 



0 10 20 30 40 50 60 70 80 90 100 



0 10 20 30 40 50 60 70 80 90 100 


Percentage of Area/Volume Exceeding the Criteria 


Percentage of AreaA/olume Exceeding the Criteria 


Figure VI-10. Light area reflects amount of 'allowable' criteria exceedance defined as the area under the 
reference curve (light line). Dark area reflects the amount of 'non-allowable' criteria exceedance defined as 
the area between the attainment curve (black line) and the reference curve. 


being evaluated and refined is the 
Kolmogorov-Smimov (KS) test, which 
was originally developed to test for signif¬ 
icant differences between cumulative 
density functions (Haan 1977). The KS 
test is nonparametric and is based on the 
maximum difference between curves 
(Figure VI-11). The maximum difference 
is somewhat different than the area 
between the curves, which is the preferred 
indicator for assessing attainment. 
However, it can be shown that the 
maximum difference and the area 
between the curves are closely correlated 
and, therefore, evaluation of one will 
reflect an evaluation of the other. 

The KS test is well-documented and 
accepted in the statistical literature. Some 
refinements that may be necessary are 
currently being evaluated. Overall, 
however, the KS test has a strong potential 
for evaluating water quality criteria attain¬ 
ment in the Chesapeake Bay. 



Percentage of AreaA/olume Exceeding the Criteria 


Figure VI-11. Illustration of the basis of the Kolmogorov- 
Smirnov statistical test for identifying statistically significant 
differences between cumulative density functions. In this case, 
the test is applied to identify statistically significant differences 
between the reference and attainment curves. 


chapter vi • Recommended Implementation Procedures 


( 



















DIAGNOSING THE MAGNITUDE OF 
CRITERIA EXCEEDANCE 

The CFD is a useful tool for evaluating water quality criteria attainment, but it is 
based on pass/fail principles and provides no information on the magnitude of 
criteria exceedance, which would interest managers, because it indicates how much 
effort is needed to correct any impairment. To fill this need and provide supporting 
information for the CFD, it is recommended that interpolator plots be generated for 
each monitoring event conducted during an assessment period. Viewed either 
individually or as a movie, interpolator plots will shed light on the magnitude of 
exceedance during the assessment period. 

Two types of interpolator plots are useful for this purpose. The first is the basic inter¬ 
polator plot of the criteria parameter (i.e., concentration for dissolved oxygen and 
chlorophyll a, and percent light-through-water for water clarity; Figure VI-12). Such 



Chlorophyll a 

Relative to Possible Criteria 

0-3 
3-0 
6-10 
10-30 
30-44 
45-53 
54-62 


Figure VI-12. Example plot of chlorophyll a concentration (/jg liter 1 ) estimates 
generated through spatial interpolation for purposes of evaluating the magnitude 
of criteria exceedance. 


chapter vi 


Recommended Implementation Procedures 







plots show problem areas and indicate their distance from criteria attainment. 
However, they are limited in evaluating the overall picture of magnitude of criteria 
exceedance for the entire Chesapeake Bay. Criteria values vary spatially and thus the 
magnitude of exceedance will depend on both actual interpolator values and the 
criteria values themselves. To address this need, a second set of interpolator plots 
illustrating the magnitude of exceedance as a percentage of the criteria values them¬ 
selves should be generated (Figure VI-13). Any estimated values below the criteria 
level will be less than one and bounded at zero, whereas estimated values above the 
criteria level will be in percentage of criteria level. 

Other information is available to evaluate the significance of the criteria attainment 
assessment results and to place them in context. This includes the size of the desig¬ 
nated use (as surface area or volume) and the percentage of the total habitat that is 
represented by the designated use. This particular data is especially useful for 
dissolved oxygen criteria attainment assessment. The information is used to under¬ 
stand the relative percentage of the total habitat that is accounted for by the 






Interpolator-Estimated 
Chlorophyll a Concentration 
(pg liter 1 ) 


Attains 

101-120 

121-120 

200-300 

300-400 

400-500 

500-600 

600-1100 


Figure VI-13. Example plot of chlorophyll a concentration (pg liter' 1 ) estimates 
generated through spatial interpolation, expressed as a percentage of a possible spring 
season criteria value, for purposes of evaluating the magnitude of criteria exceedance. 


chapter vi • Recommended Implementation Procedures 




open-water, deep-water or deep-channel designated use habitats in the entire water 
column. For example, if the deep-water use was found in nonattainment at a rate of 
50 percent but only accounted for 10 percent of the total habitat of the water column, 
the management actions taken in response would differ from those taken if the deep¬ 
water use accounted for 75 percent of the total habitat. This may prove to be a useful, 
additional source of data when difficult decisions must be made. 


DEFINING THE REFERENCE CURVE 

The recommended criteria attainment assessment approach is designed to protect the 
living resources as defined by the designated uses. The criteria levels themselves 
were largely based on scientific studies performed in laboratory settings or under 
controlled field conditions. The criteria establish the level of a given habitat condi¬ 
tion that living resources need for survival. They do not account for many other 
environmental factors that could affect survival. 

Reference curves were developed to provide a scientific-based, direct measure of the 
‘allowable’ criteria exceedances. These exceedances are defined to be those that last 
a short enough time or cover a small enough area to have no adverse affects on the 
designated use. It is assumed that the designated uses can be attained even with some 
limited level of criteria exceedances and thus, the reference curves define those 
criteria exceedances deemed to be allowable—chronic in time but over small areas, 
or infrequent occurrences over large areas. Exceedances that occur over large areas 
of space and time would be expected to have significant detrimental effects on 
biological communities, which would imply nonattainment of designated uses. 

STRENGTHS AND LIMITATIONS 

Although the Chesapeake Bay and its tidal tributaries are listed as impaired water 
bodies, there are some places that have met or usually meet the Chesapeake Bay 
criteria and support healthy aquatic living resource communities. Reference curves 
derived from monitoring these areas reveal patterns of criteria attainment or 
exceedances that support the healthy community. That is, they show whether areas 
that support a relatively healthy target community: 1) never exceed the applicable 
criteria, 2) exceed the criteria frequently, but over a small area or volume, 3) exceed 
the criteria infrequently over a large area or volume or 4) exhibit some other pattern. 

The EPA recognizes that there are currently a limited number of reference sites, given 
the Chesapeake Bay’s nutrient-enriched status. In addition, there are limited data avail¬ 
able—both for criteria parameters as well as measures of the biological health of target 
communities—with adequate spatial and temporal coverage from which to develop a 
full array of biological-based reference curves. However, where sufficient data exist, 
the reference curves appear to be stable. The reference curve for the deep-water desig¬ 
nated use dissolved oxygen criteria is the most solidly grounded in data. 


chapter vi 


Recommended Implementation Procedures 



This biological reference curve (see below for details) is based on dissolved oxygen 
concentration distributions at sites associated with bottom sediment-dwelling 
benthic communities scoring 3 or higher on the Chesapeake Bay benthic index of 
biotic integrity (benthic-IBI). If several of the reference segments were randomly 
removed, the regenerated reference curves do not change much, suggesting that 
within designated uses, the attainment curves for reference segments appear to be 
very similar. Although less firmly grounded, the reference curves for other desig¬ 
nated uses and other criteria also seem to be relatively stable. 

APPROACHES TO DEFINING REFERENCE CURVES 

At least three options exist for defining a reference curve (Figure VI-14). Fixed 
percentages could be selected based on a policy decision or other basis similar to the 
10 percent level of acceptable exceedances allowed in 305(b) EPA national guidance 
(Figure VI-14a; U.S. EPA 1997). Alternatively, laboratory or empirical field data 
from areas known to be unimpaired by the stressor can be used to derive a biologi¬ 
cally-based reference curve (Figure VI-14b). Even this second approach, however, 
requires technical or policy decisions regarding the acceptable level of biological 
effect. Finally, a reference curve could be established to reflect uncertainty based on 
the assumption of a normal distribution, and using observed or estimated error vari¬ 
ance for both time and space (Figure VI-14c). 



0 10 20 30 40 50 60 70 80 90 100 


Percentage of Area/Volume Exceeding the Criteria 


Figure VI-14. Three possible options for setting reference curves for application 
to the cumulative frequency distribution approach for defining criteria attainment: 
(a) fixed percentages based on policy decisions; (b) biological effects-based empirical 
field or laboratory data and; (c) observed or estimated uncertainty data. 


chapter vi • Recommended Implementation Procedures 














The reference curves described below for the dissolved oxygen and water clarity 
criteria are based on empirical, biologically-based field data where possible. Where 
no corroborating field data exist, a normal distribution curve representing approxi¬ 
mately 10 percent exceedance is used (see Figure VI-18). Appendix H contains 
supporting analyses and detailed descriptions of the methodologies used for defining 
these reference curves, as well as the list of reference locations. 

REFERENCE CURVES FOR DISSOLVED OXYGEN CRITERIA 

Reference curves for dissolved oxygen are intended to represent the spatial and 
temporal distribution of dissolved oxygen concentrations in areas supporting healthy 
species and communities the criteria were established to protect. The deep-water 
designated use, for example, contained the necessary water quality and biological 
source data collected over similar temporal and spatial scales. When such data were 
not available at the scales necessary to establish quantitative relationships between 
the criteria parameter and measured living resource community health, surrogate 
measures of biological and habitat conditions were explored. Ideally, each set of 
designated use-based dissolved oxygen criteria should have a separate, individually 
derived reference curve. However, satisfactory synoptic water quality and biological 
indices data or surrogate measures of habitat condition were found only for the open- 
water fish and shellfish and deep-water designated uses and were tested only against 
the 30-day mean criteria for those uses. 

Migratory Fish Spawning and Nursery Dissolved Oxygen 
Criteria Reference Curve 

Current Chesapeake Bay water quality monitoring in migratory fish spawning and 
nursery habitats is limited to midchannel stations. There also are insufficient 
spawning success fisheries-independent data available to identify biologically-based 
reference sites for these criteria. In addition, the criteria duration components for this 
designated use are an instantaneous minimum and 7-day mean, and methodologies 
to translate less frequently monitored dissolved oxygen measurements into these 
time steps have not been finalized. 

An attainment curve for exploratory purposes was created for the February-May 
spawning period, using a 30-day criterion of 6 mg liter' 1 and reference sites identified 
using nitrogen, phosphorus, chlorophyll a and total suspended solids as parameters 
(Figure VI-15). Attainment was very close to 100 percent. Until more data are 
collected to assess the attainment of the 7-day mean and instantaneous minimum 
criteria in the migratory fish spawning and nursery designated use, however, the open- 
water dissolved oxygen criteria reference curve should be applied (Figure VI-16). 

Open-Water Dissolved Oxygen Criteria Reference Curve 

In the absence of a Chesapeake Bay open-water fish community index of biotic 
integrity, reference Chesapeake Bay Program segments with ‘good’ water quality 


chapter vi 


Recommended Implementation Procedures 



o- o Percentage of Area/Volume Exceeding the Criteria 


Figure VI-15. Initial attempt at developing a dissolved oxygen criteria reference curve for 
migratory, spawning and nursery habitat designated use areas using the 6 mg liter 1 7-day 
mean criterion assessed as a 30-day mean. 




Figure VI-16. Dissolved oxygen criteria reference curve for defining criteria attainment in 
open-water designated use habitats. 


were identified based on assessments of surface and above-pycnocline concentra¬ 
tions of four parameters: total nitrogen, total phosphorus, chlorophyll a and total 
suspended solids (see Appendix F for details). Cumulative frequency distribution 
reference curves for migratory spawning and nursery designated use habitats from 
February through May (Figure VI-15) and for open-water designated use habitats in 
summer (Figure VI-16) were derived using dissolved oxygen concentration data 
from these segments. 

The Chesapeake Bay Program’s Tidal Monitoring and Analysis Workgroup devel¬ 
oped a procedure to assess relative status for cases in which an absolute point of 
reference for a water quality parameter is not available (Alden and Perry 1997). That 
procedure uses the logistic distribution of a parameter in a ‘benchmark’ data set as a 


chapter vi • Recommended Implementation Procedures 















standard against which individual data points are assessed. The individual data are 
thus scored between 1 and 100. The assessments are conducted separately in salinity 
classification and in depth layers corresponding to the designated uses. The median 
score of the individual data scores is then calculated. The benchmark distribution is 
divided roughly into thirds, which are defined as ‘good’, ‘fair’ and ‘poor’ (these 
terms relate only to each other, not necessarily to actual water quality requirements 
of living resources). Status of the parameter is assigned depending on where the 
median score falls among these divisions. 

Using this procedure, open-water concentrations of the four parameters were 
assessed for each Chesapeake Bay Program segment, yielding for each parameter an 
assessment of ‘good,’ ‘fair’ or ‘poor’ for each segment, year and season (spring and 
summer). To qualify as a reference location, at least three out of four parameters had 
to be ‘good’ and only one parameter could be ‘fair’. Once the times and locations 
were selected, the corresponding monthly average dissolved oxygen concentration 
data were evaluated against the migratory fish spawning and nursery dissolved 
oxygen criterion value of 6 mg liter' 1 (evaluated as a 30-day mean, not as a 7-day 
mean) and the open-water dissolved oxygen 30-day mean criterion of 5 mg liter' 1 for 
spring and summer, respectively. The percent volume failing the criterion was calcu¬ 
lated for each month of the season/year. The resulting cumulative frequency 
distribution curves are shown in figures VI-15 and VI-16, respectively. Figure VI-16 
currently serves as the recommended reference curve for both the migratory fish 
spawning and nursery and open-water fish and shellfish designated uses for purposes 
of assessing dissolved oxygen criteria attainment. 

Deep-Water Dissolved Oxygen Criteria Reference Curve 

Reference areas were identified using a measure of benthic community health, the 
Chesapeake Bay Benthic Index of Biological Integrity (benthic-IBI; Weisberg et al. 
1997). Sessile benthic communities are good indicators of water quality conditions of 
overlying waters. Although relatively tolerant of lower oxygen concentrations, a 
dissolved oxygen concentration of 2 mg liter' 1 is considered the lower threshold below 
which benthic infaunal communities become severely stressed (see Chapter III). A 
healthy benthic community, therefore, could indicate that dissolved oxygen conditions 
meeting deep-water dissolved oxygen criteria were met. Benthic infaunal community 
samples are collected as part of a long-term Chesapeake Bay Benthic Monitoring 
Program. Samples are collected at fixed and random locations in the summer season, 
usually in August/September. If the benthic-IBI of that sample is ‘good’, in this case 3 
or greater on a scale of 1 to 5, then it is likely that dissolved oxygen conditions have 
been adequate for the previous one to two months of the summer. 

The benthic-IBI data from 1985 through 1994 were assessed and a list of deep-water 
reference locations identified by year and segment was compiled. Then, the summer 
(June through September) dissolved oxygen data that were collected as part of the 
Chesapeake Bay Water Quality Monitoring Program at the times and places on the 
list were evaluated relative to the deep-water criteria. Figure VI-17 shows the 


chapter vi 


Recommended Implementation Procedures 



Figure VI-17. Dissolved oxygen criteria reference curve for defining criteria attainment in 
deep-water designated use habitats. 


resulting cumulative frequency distribution curve, which serves as the recommended 
reference curve for the deep-water seasonal fish and shellfish designated use for 
assessing dissolved oxygen criteria attainment (see Appendix H for documentation 
of the validation curves used to confirm the reference curve). 

Deep-Channel Dissolved Oxygen Criteria Reference Curve 

The deep-channel seasonal refuge designated use contains dissolved oxygen concen¬ 
trations that are inadequate to support most Chesapeake Bay species, and the 
criterion is set to protect the survival of benthic organisms. Unfortunately, a biolog¬ 
ically-based reference curve could not be developed for the deep-channel use at this 
time. This area is assumed to be severely degraded and is not now sampled as part 
of the Chesapeake Bay Program long-term benthic monitoring program. No other 
appropriate biological data were available with which to identify reference sites. 

While a biologically-based reference curve is recommended for the future, a default 
reference curve such as the normal distribution curve representing approximately 10 
percent exceedance is appropriate in this case to account for anticipated natural 
criteria exceedances (Figure VI-18). States and other users must recognize that the 
deep-channel dissolved oxygen criterion is stated as an instantaneous minimum, thus 
any exceedance is assumed to have direct consequences to the survival of the 
bottom-dwelling community. 

REFERENCE CURVES FOR WATER CLARITY CRITERIA 

Reference areas for development of the water clarity criteria reference curve were 
identified as Chesapeake Bay Program segments or parts of segments where under¬ 
water bay grasses were abundant historically and thriving or increasing in coverage 


chapter vi • Recommended Implementation Procedures 















0 10 20 30 40 50 60 70 80 90 100 


Percentage of Area/Volume Exceeding the Criteria 


Figure VI-18. Cumulative frequency distribution curve in the shape of a hyperbolic curve 
that represents approximately 10 percent allowable exceedances equally distributed 
between time and space. 


in recent years. Separate reference curves were developed for low salinity—tidal- 
fresh and oligohaline-and higher salinity-mesohaline and polyhaline-zones. The 
supporting analyses for deriving the water clarity criteria reference curves are 
provided in Appendix H. 

Once the reference Chesapeake Bay Program segments were identified, the water 
clarity data (as measured by Secchi depth) for those segments were extracted from 
the Chesapeake Bay water quality monitoring program data base. Percent light- 
through-water (PLW) is the operational parameter used for assessing attainment of 
the water clarity criteria. PLW = 100exp(-K d Z), where Z is the target restoration 
depth and K d , the coefficient of extinction, is estimated as K d = 1.45/Secchi depth 
(see Chapter III for details). K d values calculated from the Secchi depth data were 
averaged by month for each station. The monthly data were then spatially interpo¬ 
lated bay wide for each month in the underwater bay grass growing season from 1985 
through 1994 to match the Chesapeake Bay water quality model hydrologic simu¬ 
lation period. PLW was calculated for each interpolation cell using the interpolated 
K d value and the defined segment-specific restoration depth. The PLW values were 
compared to the criterion value appropriate to the Chesapeake Bay Program 
segment’s salinity zone, and the percent of the shallow-water area (< 2 meters) 
failing the criterion in each segment was calculated for each month. The monthly 


chapter vi • Recommended Implementation Procedures 







attainment percentages for each reference Chesapeake Bay Program segment were 
pooled in their respective low and higher salinity groups and plotted as cumulative 
frequency distribution curves (figures VI-19 and VI-20). Appendix H contains the 
reference curves generated using the more recent 1995-2000 data. All these water 
clarity criteria reference curves were derived using data spanning decadal scales, 
capturing the full range of wet, dry and average hydrologic conditions. 

The derived water clarity criteria reference curves reflect findings published in the 
scientific literature for Chesapeake Bay species that indicate that underwater plants 
can survive reduced light conditions for periods of days to weeks. Field and labora¬ 
tory experiments indicated that lower salinity species were more tolerant of longer 
periods of reduced light conditions (Rybicki et al. 2002) compared with species 
inhabiting higher salinity waters (Goldsborough and Kemp 1988). These salinity 
regime differences also are reflected in the different shapes of the derived reference 
curves. The lower salinity reference curve allows for more exceedances over time 
and space than are allowed for by the higher salinity reference curve (figures VI-19 
and VI-20, respectively). 

It should be noted that the water clarity criteria were derived, in part, on the basis of 
underwater bay grass growing season medians (Batiuk et al. 1992, 2000), but 
attainment is measured on a monthly basis over the growing season (see “Devel¬ 
oping the Cumulative Frequency Distribution,” p. 154, for details). Appendix H also 
shows water clarity reference curves based strictly on growing season median 
assessments. 



o Percentage of Area/Volume Exceeding the Criteria 


Figure VI-19. Water clarity criteria reference curve for defining criteria attainment in 
tidal-fresh/oligohaline shallow-water bay grass designated use habitats. 


chapter vi • Recommended Implementation Procedures 













Figure VI-20. Water clarity criteria reference curve for defining criteria attainment in 
mesohaline/polyhaline shallow-water bay grass designated use habitats. 


REFERENCE CURVES FOR CHLOROPHYLL A CRITERIA 

As states derive numerical regional and local specific chlorophyll a criteria, they 
should either derive biologically-based reference curves that reflect the ‘allowable’ 
exceedances of local impairments or apply the normal distribution curve representing 
approximately 10 percent ‘allowable’ exceedance in time and space (see Figure VI-18). 

The cumulative frequency distributions derived from the subset of Chesapeake Bay 
water quality monitoring program chlorophyll a data associated with the ‘Better’ and 
‘Best,’ and sometimes ‘Mixed_Better Light’ water quality categories closely 
matched the normal distribution curve in both spring and summer (figures VI-21 and 
VI-22). These categories formed the basis for characterizing the Chesapeake Bay 
phytoplankton reference community (see Chapter V and Appendix F for details). The 
cumulative frequency distributions were derived from applying the 95th percentiles 
of chlorophyll a values occurring in these categories (see Table V-6). In figures 
VI-21 and VI-22, respectively, the cumulative frequency distributions of spring 
(March-May) and summer (July-September) chlorophyll a concentration exceeding 
the 95th percentile phytoplankton reference community values (a) are overlaid with 
the normal distribution curve (b). The normal distribution curve matches well with 
both seasonal biological-based cumulative frequency distributions, providing further 
justification for applying the normal distribution curve as a chlorophyll a criteria 
reference curve in the absence of a directly derived biological reference curve. 

REFERENCE CURVE IMPLEMENTATION 

As the states adopt the Chesapeake Bay criteria and concomitant procedures into 
their water quality standards, they may decide to: 1) allow for no criteria exceedance, 
2) select the normal distribution curve representing approximately 10 percent 
allowable criteria exceedance or 3) apply a biological reference curve. The first two 


chapter vi • Recommended Implementation Procedures 










0 20 40 60 80 100 


Percentage of Area/Volume Exceeding the Criteria 


Figure VI-21. Cumulative frequency distribution of spring (March-May) chlorophyll a 
concentration exceeding the 95 th percentile phytoplankton reference community 
values (a) compared with the normal distribution curve (b). 



Figure VI-22. Cumulative frequency distribution of summer (July-September) chlorophyll 
a concentration exceeding the 95 th percentile phytoplankton reference community values 
(a) compared with the normal distribution curve (b). 


chapter vi • Recommended Implementation Procedures 




















options are likely to be more restrictive than the biological reference curve approach. 
If states choose to apply the biological reference curve, then there should be a strong 
incentive to collect relevant data to strengthen the scientific basis of those reference 
curves in the future. 


MONITORING TO SUPPORT THE ASSESSMENT 
OF CRITERIA ATTAINMENT 

To support the development of cumulative frequency distributions for criteria attain¬ 
ment assessment purposes, additional monitoring will be required. The current 
fixed-station Chesapeake Bay Water Quality Monitoring Program will support many 
aspects of the effort to assess criteria attainment. However, some aspects will require 
new monitoring in areas of Chesapeake Bay tidal waters from which data have not yet 
been collected. Other aspects will require new types of monitoring based on new 
technologies that will better address the technical requirements of the criteria as they 
are currently defined. The Chesapeake Bay Program has developed a tidal monitoring 
network design that identifies the needs and proposes options for addressing those 
needs. Many of those options can feasibly be implemented, but additional monitoring 
will be expensive, and it is expected that available funds will limit what can be done. 

The following describes options for conducting monitoring to support the assess¬ 
ment of criteria attainment. Given that funding may be limited, the monitoring 
options are divided into three categories based on funding level. The first category, 
‘recommended’, assumes that funding will be available to conduct monitoring to 
fully support the assessment of criteria attainment. The ‘recommended’ level of 
monitoring is based on technological needs to provide a set of data that can be 
defended legally and scientifically in making decisions regarding the attainment of 
designated uses. The second category, ‘adequate’, assumes that funding will be 
somewhat limited, but will be sufficient to collect enough data to support the devel¬ 
opment of cumulative frequency distributions for most criteria components in most 
Chesapeake Bay Program segments and tidal-water designated uses. The third cate¬ 
gory, ‘marginal’, assumes that monitoring will be significantly limited by available 
funding and that it will not be possible to assess all criteria components in all 
segments of the Chesapeake Bay and its tidal tributaries. 

Efforts are underway to develop the tools necessary to generate verifiable and quan¬ 
titative estimates of error and the levels of monitoring required for given levels of 
accuracy acceptable to management agencies. The three general categories defined 
above were developed to give the reader some perspective on the range of options 
available and the adequacy of the options. 

SHALLOW-WATER MONITORING 

Resource managers rely upon habitat and water quality monitoring data to charac¬ 
terize problem areas in a watershed, such as areas of low dissolved oxygen, and to 


chapter vi 


Recommended Implementation Procedures 




detect changes related to management strategies to reduce nutrients and sediments 
on a tributary to baywide level. Traditional monitoring programs have collected peri¬ 
odic data at a small number of fixed sampling locations, often in the deeper 
midchannel areas. These measurements provide a good baseline for watershed 
assessment and long-term trends, but may miss small-scale gradients in tidal water 
quality and neglect critical shallow-water habitats. 

In the past, intensive water quality monitoring of these shallow-water habitats has 
been time-intensive and cost-prohibitive. The advent of a new suite of technologies 
known as the DATAFLOW water quality monitoring system, however, has brought 
intensive monitoring of shallow-water habitats into reach (http://mddnr. 
chesapeakebay.net/sim/index.html). DATAFLOW is a system of shipboard water 
quality probes that measure spatial position, water depth, water temperature, salinity, 
dissolved oxygen, turbidity (a measure of clarity of the water) and chlorophyll a 
from a flow-through stream of water collected near the water body’s surface. The 
system allows data to be collected rapidly (approximately every four seconds) and 
while the boat is traveling at speeds up to 25 knots. Because the DATAFLOW system 
is compact, it can be housed on a small boat, enabling sampling in shallow water and 
the ability to map an entire small tributary in less than a day. Typical DATAFLOW 
research cruise sampling paths traverse shallow and channel areas to obtain a full 
characterization of a tributary’s water quality. 

The discussion below focuses on migratory spawning and nursery, open-water, deep¬ 
water and deep-channel designated uses. The DATAFLOW system is the only viable 
option for monitoring water quality conditions in the shallow-water designated use. 
The high temporal and spatial variability expected in shallow-water areas implies 
that intensive data collection would be required for any assessment to have credi¬ 
bility. A probability-based approach was considered as a less expensive approach for 
shallow-water monitoring, but the cost savings were not sufficient to justify the 
reduced amount of information that this approach would provide. The only option 
for reduced costs in shallow-water monitoring is to limit the amount that is 
conducted during any one year. 

DISSOLVED OXYGEN CRITERIA ASSESSMENT 
'Recommended' Level of Monitoring 

Monitoring for dissolved oxygen criteria attainment should address all four frequen¬ 
cies of dissolved oxygen criteria: 30-day mean, 7-day mean, 1-day mean and 
instantaneous minimum. The current fixed-station monitoring program is designed 
to provide a long-term record of dissolved oxygen concentrations that reflect 
seasonal and interannual variation. For that reason, even though instantaneous meas¬ 
urements are collected, the current monitoring is best suited for assessing the 30-day 
mean dissolved oxygen criteria component and poorly suited for assessing the 7-day, 
1-day mean and instantaneous minimum criteria components. To address the need 
for data that will address the 7-day, 1-day mean and instantaneous minimum criteria 


chapter vi • Recommended Implementation Procedures 



components, continuous monitors mounted to buoys or piers will be required. At 
least one continuous monitor should be located at each assessment location. The 
continuous record will then be combined with fixed-station data, used to calibrate 
the spectral-analysis model (described below), and all criteria components could be 
quantified using that model. Individual criteria component estimates would be 
assessed at all fixed locations and interpolated for incorporation in a cumulative 
frequency distribution. 

'Adequate' Level of Monitoring 

Assuming that funding will not be available for the ‘recommended’ monitoring 
approach, an alternative would be to place a limited number of continuous monitors 
at representative locations in the Chesapeake Bay and tidal tributaries. The number 
of continuous monitors would be relatively small, but the number would be estab¬ 
lished to characterize different types of settings in the Chesapeake Bay. Those 
representative temporal records would then be combined with fixed-station data in 
similar settings, and spectral models would be developed for each fixed-station loca¬ 
tion. Dissolved oxygen criteria components would be assessed based on the spectral 
models, interpolated and used to develop the cumulative frequency distributions. 
This approach would entail much greater uncertainty in the assessments. The 
absolute variation would be characterized well by regular monthly measurements at 
the fixed-stations. However, the higher frequency assessments would be based on 
data collected at only a few locations, which would then be extrapolated over large 
distances. 

'Marginal' Level of Monitoring 

Assuming that funding will not be available for even the ‘adequate’ level of moni¬ 
toring, assessments would need to rely on the fixed-station data only. As stated 
above, this type of monitoring was designed for long-term assessments and would 
only be truly appropriate for the 30-day mean criteria component. If the ‘marginal’ 
level of monitoring was selected, it is likely that higher frequency criteria compo¬ 
nents would not be assessed in most designated use areas. 

Assessing Dissolved Oxygen Criteria Attainment 

Addressing Duration Issues. The dissolved oxygen criteria have several 
different durations: 30-day mean, 7-day mean, 1-day mean (deep-water only) and 
instantaneous minimum. A state’s ability to assess these criteria and to have certainty 
in the results depends on the time scale of available data and on the capacity of 
models to estimate conditions at those time scales. At present, long-term, fixed- 
station, midchannel water quality monitoring in the Chesapeake Bay and its tidal 
tributaries provides dissolved oxygen measurements twice monthly at most or 
approximately every 15 days between April and August. Proposed enhancements to 
the tidal water quality monitoring program include shallow-water monitoring, as 


chapter vi 


Recommended Implementation Procedures 


well as high-resolution spatial and temporal monitoring in selected locations. 
However, these new components are only in the planning and early implementation 
stages at this point, and because of financial constraints or limitations to current 
technology, direct monitoring at the scales of the criteria may not be possible in the 
foreseeable future. Therefore, the direct assessment of attainment for some 
geographic regions and for some short-term criteria elements (e.g., instantaneous 
minimum, 1-day mean and 7-day mean) must be waived for the time being or based 
on statistical methods that estimate probable attainment. Several approaches to 
addressing the duration issue are described below. 

Thirty-Day Mean Attainment Procedure. This duration appears to be within the 
temporal scale of the current Chesapeake Bay water quality monitoring programs. 
The simplest assessment approach is to use the one value or average of two values 
collected within a month as the best estimate of the true 30-day mean. At present, 
this is the approach recommended for assessing attainment of criteria with this dura¬ 
tion. However, it is debatable how well one or two samples per month represent what 
is intended as protective by the 30-day mean. 

These procedures assume the existence of a baywide tidal-water monitoring program 
with a fixed-station sampling design and sampling frequency at least once per month 
during the seasons defined by the criteria. The procedures assume that horizontal and 
vertical measurements of dissolved oxygen will be sufficiently dense that the inter¬ 
polator can create an accurate three-dimensional representation of dissolved oxygen 
in the defined designated uses. It also assumes that data are sufficient to define the 
boundaries of the designated uses where boundaries are variable, depending on 
pycnocline depth. 

To simplify computations, if there is more than one observation per month, then the 
monthly average is calculated prior to input to the volumetric interpolator. Prior to 
averaging for the month, each station’s dissolved oxygen profile is interpolated verti¬ 
cally to obtain a value at each half-meter interval from surface to bottom. The 
monthly average concentrations at each fixed station at each half-meter are then 
interpolated horizontally by the Chesapeake Bay interpolator to yield a basinwide 
grid of concentrations for each month. A comparable reference grid or a table of grid 
coordinates and depths can be used to relate the monthly cell concentrations to be 
evaluated with the correct designated use and corresponding criteria concentrations. 
The cell is scored as meeting or not meeting the criterion level and cell volume is 
accumulated in the pool of passing or failing totals for each designated use in each 
Chesapeake Bay Program segment. From this, the spatial extent of nonattainment, 
i.e., the percentage of the total volume exceeding the criterion in each designated use 
in each Chesapeake Bay Program segment is tallied for each month in the assess¬ 
ment period (most recent three years). 

Dissolved oxygen criteria attainment is reported seasonally (see Table VI-1). To 
assess, for example, attainment of the summer season 30-day mean criterion for the 
deep-water seasonal fish and shellfish designated use, the percent exceedance data 


chapter vi • Recommended Implementation Procedures 


for the months of June through September for a three-year period for all Chesapeake 
Bay Program segments with deep-water designated use habitats would be extracted 
and evaluated individually using the cumulative frequency distribution approach. 
The cumulative frequency distribution attainment curve would be calculated (and 
plotted, if desired) and compared to the appropriate reference curve for the desig¬ 
nated use and season using the statistical test described earlier. If the two curves are 
significantly different, then the segment/designated use is considered out of attain¬ 
ment, and failing by the amount defined by the area between the two curves. 

Seven-Day Mean Attainment Procedure. The 7-day time frame is much shorter 
than the temporal scale of the current baywide water quality monitoring programs, 
and statistical forecasting models are necessary to assess criteria of this duration. 
The proposed approach, referred to as the spectral analysis approach in this chapter 
and discussed in more detail below, uses long-term, low-frequency data from the 
monitoring program and shorter-term, high-frequency data from in situ semi-contin¬ 
uous monitors to synthesize a data set that incorporates both long- and short-term 
patterns of variability. The synthetic data set is created at user-specified time inter¬ 
vals, e.g., weekly, daily and hourly. The minimum interval will depend on the 
interval length of the continuous data. The synthetic data set is then analyzed at the 
appropriate temporal scale, which in this case is seven days. At present there are 
insufficient high-frequency data and insufficient validation of the approach to 
recommend its implementation. For now, attainment of 7-day mean criteria should 
not be assessed unless data are available for a specific location/segment at a temporal 
scale consistent with the 7-day duration. 

One-day Mean Attainment Procedure. The 1-day attainment procedure is the 
same as the 7-day mean procedure described above. For now, attainment of the 
1-day mean criteria should not be assessed unless data are available for a specific 
location/segment at a temporal scale consistent with the 1-day duration. 

Instantaneous Minimum Attainment Procedure. Again, the instantaneous 
minimum time frame is much shorter than is currently sampled. The spectral 
analysis approach presented above is one way to estimate attainment of these 
dissolved oxygen criteria. Another approach, referred to as the logistic regression 
approach in this chapter and described in more detail below, applies by restating the 
criterion in slightly different temporal terms. An instantaneous minimum implies 
that the criterion is not met if dissolved oxygen concentrations are below the crite¬ 
rion value at any time. The logistic regression approach estimates the relative 
frequency or percent of time that a region falls below a specified concentration based 
on the empirical relationship between seasonal or monthly mean values and the 
percent of dissolved oxygen concentrations above or below the specified level as 
observed in the historical data record (of the Chesapeake Bay water quality moni¬ 
toring program). This method has been applied experimentally with reasonable 
results (Jordan et al. 1992) and can approximate criteria exceedance/attainment 
frequency. However, at this time the method has not been adequately validated to 
recommend implementation for formally assessing criteria attainment. Attainment of 


chapter vi • Recommended Implementation Procedures 


instantaneous minimum criteria should not be assessed unless data are available for 
a specific location/segment at a temporal scale consistent with the instantaneous 
minimum duration. 

Spectral Analysis Approach. The foundation for this method was developed by 
Neerchal et al. (1992) in the context of implementing the Chesapeake Bay dissolved 
oxygen restoration goal (Jordan et al. 1992) and has been modified for criteria appli¬ 
cation. The method uses spectral analysis to extract the cyclical components of the 
long- and short-term time-series records and combines them to create a synthesized 
time-series data set with data synthesized at user-specified time steps. At present, the 
synthetic data are hourly, with cyclic components limited to two cycles per day. The 
synthetic data have the annual and seasonal cyclic and trend characteristics of the 
long-term record as well as the tidal, diurnal and any other periodic characteristics 
of the short-term, high-frequency record. The long-term record comes from fixed- 
station monitoring data collected at regular once or twice monthly intervals in the 
seasons of interest. The short-term data come from in-situ semicontinuous oxygen 
monitors deployed on buoys or other fixed structures at designated locations around 
the Chesapeake Bay and its tidal tributaries. These semicontinuous oxygen monitors 
are put in place for various lengths of time at many different locations and depths. 
Sites are chosen in order to best characterize the dissolved oxygen conditions in each 
designated use. The sampling interval of the semicontinuous monitors are commonly 
5, 10 or 20 minutes. To be most useful, the interval should be no longer than one 
hour. More details are provided in Appendix I. 

Application of the Spectral Analysis Approach. The spectral analysis application 
shown in Figure VI-23 uses long-term data from station CB4.2C, a monitoring 
station in the midregion of the Chesapeake Bay, and a two-month series of contin¬ 
uous dissolved oxygen measurements at a buoy deployment near that station at 
approximately 9 meters below the surface. Figure VI-23 shows the observed monthly 
dissolved oxygen concentrations (asterisks) at station CB4.2C (8- to 10-meter depth) 
and the long-term forecast (line) from the spectral equation. 



Figure VI-23. Observed monthly dissolved oxygen concentrations (*) at Chesapeake Bay 
Monitoring Program station CB4.2C (at the 8 to 10 meter depth) from January 1985 to 
January 2000 and the long-term 'forecast' (—) from application of the spectral equation. 


chapter vi • Recommended Implementation Procedures 













The synthetic data record is obtained by combining the long- and short-term equa¬ 
tions. A sample two-month period, August through September 1987 (indicated by 
the two, close-together vertical reference lines in Figure VI-23), is illustrated in 
Figure VI-24. This synthetic record can then be analyzed relative to the applicable 
criteria elements. In the example shown, the 9-meter depth at station CB4.2C is near 
or below the pycnocline and is, therefore, subject to criteria for the deep-water desig¬ 
nated use. Summer dissolved oxygen criteria for the deep-water designated use is a 
3 mg liter 1 30-day mean, 2.3 mg liter 1 1-day mean and 1.7 mg liter 1 instantaneous 
minimum. For demonstration purposes, let a 7-day mean of 2.5 mg liter' 1 also apply. 


© 
■*—< 

o> 

E 

c 

CD 

CD 

>. 

X 

O 

X5 

a> 

_> 

o 

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i6- 

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4 

04 




>|i ,111,1 , n i,i. i t i r i| ri"i" p r" i 'i | r ,,,, n-f ^ nrT'rrrrrri 1 

01AUG1987 11 AUG1987 21AUG1987 31AUG1987 10SEP1987 20SEP1987 30SEP1987 

Date 

Dot=observed monthly dissolved oxygen; dashed line=long-term forecast; solid line=combined forecast 


Figure VI-24. Expanded view from Figure VI-23 of the two-month period August- 
September 1987 synthetic data record obtained by combining the long- and short-term 
spectral equations. 


Based on monitoring data alone (two observations each month), the August and 
September mean monthly values are 3.4 mg liter 1 and 4.2 mg liter 1 , respectively. 
Basing assessment on the synthetic data record, attainment can be measured either 
in sequential or rolling time windows, as described below. In some cases the results 
vary depending on which option is used (Table VI-5). For the 30-day duration, the 
sequential option results in two 30-day periods within the 61 days, between August 
1 and September 30, 1987; the rolling time window option yields 31 periods. If there 
was a 7-day criterion for deep-water designated use, there would be 8 sequential 
versus 55 rolling-window periods in those 61 days. For the 1-day minimum duration, 
the question of sequential and rolling-window is moot. 

Verifying the Spectral Analysis Approach. The number and distribution of high 
frequency semicontinuous dissolved oxygen data sets is small compared to the 
variety of habitats, times of year and layers of the water column that need to be char¬ 
acterized. There are gaps in critical seasons, geographic coverage and designated 


chapter vi 


Recommended Implementation Procedures 













Table VI-5. Sample attainment results when assessing with varying time windows 


Dissolved Oxygen Criterion 

Time Windows 
Meeting Criterion 

Percent of 
Observations 
at or above Criterion 

30-day Mean (3 mg liter' 1 ): 



Sequential 

2 of 2 

100% 

Rolling window 

31 of 31 

100% 

7-day Mean (2.5 mg liter' 1 ): 



Sequential 

7 of 8 

87.5% 

Rolling window 

46 of 55 

83.6% 

Instantaneous Minimum (1.7 mg liter' 1 ) 



Pool of hourly measurements 

1,250 of 1,484 

84.2% 


uses. Nevertheless, the number of such data sets on hand is substantial and growing, 
relative to the number and location of fixed monitoring stations. 

Developing and verifying the method will be an ongoing process. Short-term fore¬ 
casts based on synthetic data are created and compared to actual semicontinuous 
records not used in the original forecasting process. There are some, but not many, 
instances in which semicontinuous data are available at the same site in different 
years. Also, in some instances, multiple semicontinuous records are available for the 
same region. In these cases, one record is used in the spectral analysis and equation 
development and the other is used to verify the results. With data recorders deployed 
for the specific purpose of validating and refining the forecasting models, better veri¬ 
fication will be available in the future. 

Even with these issues resolved, there are still questions concerning how synthetic 
time-series data sets should be adapted to enable an assessment of spatial extent and 
frequency of attainment in a manner consistent with criteria assessed by other analyt¬ 
ical methods. 

Logistic Regression Approach. This method modifies and significantly updates 
a method developed originally to measure attainment of the 1992 Chesapeake Bay 
dissolved oxygen restoration goal (Jordan et al. 1992). The early work demonstrated 
predictable relationships, on a segment-by-segment basis, between seasonal mean 
dissolved oxygen concentrations and the percent of observations above a target 
concentration. The relationships proved to be strong and applicable in areas where 
dissolved oxygen concentrations ranged above and below the goal target concentra¬ 
tions. Given the tidal water quality monitoring data record that spans more than 17 
years with the measurements from multiple depths (the vertical dissolved oxygen 
profile is collected at 1- to 2-meter intervals), the regression models are now month- 
and depth-specific in many segments. Based on the monthly mean dissolved oxygen 
concentration measured at a specified depth, the models predict the percent of time 


chapter vi • Recommended Implementation Procedures 


c 









that the dissolved oxygen concentration at that depth in a segment is at or above a 
user-specified concentration, e.g., an instantaneous minimum of 1.7 mg liter 1 (see 
Appendix I for more details). 

Application of the Logistic Regression Approach. The method can be applied 
using the three-dimensional baywide interpolations of monthly average dissolved 
oxygen, as described for the determination of 30-day duration criteria. The monthly 
average concentrations at each fixed station at each half-meter are interpolated hori¬ 
zontally by the Chesapeake Bay interpolator to yield a basinwide grid of 
concentrations for each month. A comparable reference grid or a table of grid coor¬ 
dinates and depths relate the monthly cell concentrations to be evaluated with the 
correct designated use and corresponding criteria concentration (e.g, instantaneous 
minimum of 1.7 mg liter' 1 ). In the data processing step, a segment- and criterion 
level-specific prediction model uses the cell’s monthly average concentration, depth 
and month as factors in predicting the percent of the time that particular cell is at or 
above the criterion. The cell is scored as passing or failing the criterion level 
depending on the model results. The cell volume is accumulated in the pool of 
passing or failing totals for each designated use in each segment. Like the method 
for assessing the 30-day mean, the spatial extent of nonattainment, i.e., the 
percentage of the total volume exceeding the criterion in each designated use in each 
segment, is tallied for each month in the assessment period (most recent three years). 
The cumulative frequency distribution attainment and reference curves can then be 
derived, and the same statistical test for determining attainment as described for the 
direct assessment method can be applied. 

Strengths and Current Limitations. The logistic models are based on conditions 
represented by the fixed stations in the current monitoring program, which in most 
tributaries are sited in the main channel. Until more data are collected, the similarity 
of shallow areas to the midchannel in the same segment is not known. This approach 
would assume, in the absence of other data, that the main channel data are represen¬ 
tative of similar depths in the shallows. If salinity or other physical data from the 
shallows indicate that all or part of the shallow water column is more similar to a 
different depth in the midchannel (as is sometimes the case for various reasons), then 
the more representative depth would be used to estimate percent attainment. For 
example, the pycnocline typically is deeper in the portion of the Chesapeake Bay 
than on the flanks, and the depth of the pycnocline on one flank is typically deeper 
than the other. Thus a 4-meter-deep, above-pycnocline water parcel on one flank 
may be most similar to the 4-meter-above-pycnocline depth in the midchannel 
profile, while the 4-meter-deep, subpycnocline parcel on the opposite flank is more 
similar to the 5-meter depth in the midchannel profile. 

To date, dissolved oxygen concentrations have shown little significant trend in most 
areas of the Chesapeake Bay and its tidal tributaries and, therefore, history-based 
estimation models are reasonable. However, where significant trends are detected, it 
would be important to review the models and their basis in light of new, emerging 
empirical relationships at those locations. This approach provides an estimate of the 


chapter vi • Recommended Implementation Procedures 



amount of time a water parcel is above or below a particular concentration, but does 
not address the length of individual exposure, rate of re-exposure, or a specific event- 
duration such as daily or 7-day mean. 

WATER CLARITY CRITERIA ASSESSMENT 
'Recommended' Level of Monitoring 

Because the DATAFLOW technology is the only viable approach for assessing water 
quality conditions in shallow-water designated use areas, there is only a ‘recom¬ 
mended’ level of monitoring for assessing the water clarity criteria. Significant 
spatial and temporal variability are expected in the shallow-water designated use 
area. The DATAFLOW is best suited to address the high level of variability and 
provide data for credible assessments of criteria attainment. The only option for 
reduced costs in shallow-water monitoring is to limit either the total number of tidal 
systems assessed and/or the frequency of monitoring events for each system that are 
conducted during a single year. 

Assessing Attainment of the Shallow-Water Bay Grass 
Designated Use 

Restoring underwater bay grasses to areas supporting “the propagation and growth 
of balanced, indigenous populations of ecologically, recreationally and commer¬ 
cially important fish and shellfish inhabiting vegetated shallow-water habitats” is 
ultimately the best measure of attainment of the shallow-water bay grass designated 
use. To determine the return of water clarity conditions necessary to support restora¬ 
tion of underwater grasses and, therefore, attainment of the shallow-water designated 
use, states may: 1) evaluate the number of acres of underwater bay grasses present 
in each respective Chesapeake Bay Program segment, comparing that acreage with 
the segment’s bay grass restoration goal acreage; and/or 2) determine the attainment 
of the water clarity criteria within the area designated for shallow-water bay grass 
use. The shallow-water bay grass use designated use area may be defined by either: 
1) applying the appropriate water clarity criteria application depth (i.e., 0.5, 1 or 2 
meters) along the entire length of the segment’s shoreline (with exception of those 
shoreline areas determined to be underwater bay grass no-grow zones; see U.S. EPA 
2003 for details); or 2) determining the necessary total acreage of shallow-water 
habitat within which the water clarity criteria must be met using a salinity regime 
specific ratio of underwater bay grass acres to be restored within a segment to acres 
of shallow-water habitat that must meet the water clarity criteria within the same 
segment (regardless of specifically where and at what exact depth those shallow 
water habitat acreages reside within the segment). These approaches to assessing 
attainment of the shallow-water bay grass designated use are described below in 
more detail. 

Assessing Underwater Bay Grasses Restoration. In response to a commit¬ 
ment in the Chesapeake 2000 agreement, the Chesapeake Bay watershed partners 


chapter vi • Recommended Implementation Procedures 


) 



adopted a bay wide underwater bay grasses restoration goal of 185,000 acres. This 
baywide restoration goal was established “to reflect historic abundance, measured as 
acreage and density from the 1930s to present” ( Chesapeake 2000, Chesapeake 
Executive Council 2000). 

The single best year of underwater bay grasses growth observed in each Chesapeake 
Bay Program segment from the entire available record of aerial photographs (1938- 
2000) was determined by state and federal agency resource managers and 
Chesapeake Bay scientists as the best available data on underwater bay grasses 
occurrence over the long-term. The underwater bay grasses goal acreage was set 
using the single best year acreage out to a Chesapeake Bay Program segment- 
specific application depth determined as summarized in Table VI-6 and described in 
detail in the Technical Support Document for the Identification of Chesapeake Bay 
Designated Uses and Attainability (U.S. EPA 2003). Based on the implementation 


Table VI-6. Methodology for establishing the 185,000 Chesapeake Bay baywide 
underwater grasses restoration goal. 


The baywide underwater bay grasses goal acreage was set using the single best year 

acreage out to an application depth determined as follows: 

1. Bathymetry data and aerial photographs were used to divide the single best year 
underwater bay grasses acreage in each Chesapeake Bay Program segment into three 
depth zones: 0-0.5 meters, 0.5-1.0 meters and 1-2 meters. 

2. The aerial photographs were then used to determine the maximum depth to which the 
underwater bay grass beds grew in each segment with either a minimum abundance or 
minimum persistence. The underwater bay grass goal for a Chesapeake Bay Program 
segment is the portion of the single best year acreage that falls within this determined 
depth range. The decision rules for this were as follows: 

In all segments, the 0-0.5 meter depth interval was designated for shallow-water 
bay grass use. In addition, the shallow-water bay grass use was designated for 
greater depths within a segment if either: 

A) The single best year of underwater bay grasses distribution covered at least 
20 percent of the potential habitat in a deeper zone; or, 

B) The single best year of underwater bay grasses distribution covered at least 
10 percent of the potential habitat in the segment-depth interval, and at least 
three of the four five-year periods of the more recent record (1978-2000) 
show at least 10 percent SAV coverage of potential habitat in the segment- 
depth interval. 

3. The single best year underwater bay grasses distribution acreage of all Chesapeake 
Bay Program segments were clipped at the deeper depth of the segment-depth 
interval, determined above. The resulting underwater bay grass acreages for each 
segment were added, resulting in the total baywide underwater bay grass restoration 
goal of 185,000 acres. 

Source: U.S. Environmental Protection Agency 2003 


) 


chapter vi 


Recommended Implementation Procedures 




of this methodology, each Chesapeake Bay Program segment (see Figure VI-1 and 
Table VI-4) has an underwater bay grass restoration goal acreage, with the exception 
of those segments documented as underwater bay grass no-grow zones along their 
entire shoreline, with the total acreage summed up from all segments equaling 
185,000 acres. 

In adopting and implementing their water quality standards for protecting the 
shallow-water bay grass designated use, states may: 1) adopt the segment-specific 
underwater bay grass restoration goal acreages that make up the baywide 185,000 
restoration goal; or 2) adopt a lower initial Chesapeake Bay Program segment- 
specific underwater bay grass acreage, below the established goal acreage for a 
segment, and use their upcoming triennial reviews of state water quality standards to 
continually evaluate and appropriately increase the segment-specific acreages 
towards the ultimate underwater bay grass restoration goal acreage. If states choose 
to adopt a lower initial segment-specific acreage, at a minimum they must adopt an 
underwater bay grass acreage for that Chesapeake Bay Program segment equal to or 
greater than the existing use underwater bay grasses acreage defined as either the 
single best year of composite acreage of underwater bay grasses mapped through the 
baywide underwater bay grasses aerial survey since 1975. The Chesapeake Bay 
Program segment-specific acreages that, added together, make up the baywide 
185,000 restoration goal are documented in the Technical Support Document for the 
Identification of Chesapeake Bay Designated Uses and Attainability along with the 
segment-specific existing use underwater bay grasses acreages (U.S. EPA 2003). 

Achieving the Chesapeake Bay Program segment-specific underwater bay grass 
restoration acreages should be measured as the single best year of acreage as 
observed through the most recent three years of data from the Chesapeake Bay 
underwater bay grasses aerial survey. All mapped acreages of underwater bay 
grasses in a segment should be counted towards achievement of each segment- 
specific restoration goal regardless of the depth. Chesapeake Bay segment level 
acreages of underwater bay grasses are published annually and can be accessed 
through the Chesapeake Bay Program’s web site at http://www.chesapeakebay. 
net/data, or directly through the Virginia Institute of Marine Science’s “Bay Grass in 
Chesapeake Bay and Delmarva Peninsula Coastal Bays” web site at http://www. 
vims.edu/bio/sav/index.html. 

Assessing Water Clarity Criteria Attainment at an Established Applica¬ 
tion Depth. The recommended method for assessing water clarity criteria 
attainment is, first, to interpolate monthly values of to obtain a K d value for each 
interpolator cell, then to calculate PLW for each cell using the interpolated value of 
IQ and the Chesapeake Bay Program segment-specific shallow-water bay grass 
designated use boundary depth (see U.S. EPA 2003 for a full listing of the recom¬ 
mended shallow-water bay grass designated use boundary depths). Note that for 
statistical reasons, the interpolations are performed using a log transformation of the 
light values (logfK^). The resulting interpolated cell values are converted back to 
their untransformed status for the PLW calculation. 


chapter vi • Recommended Implementation Procedures 


< 


As described previously in this chapter, the interpolator cells can be associated with 
the proper Chesapeake Bay Program segment and salinity zone so that each cell’s 
PLW value can be compared to the proper salinity regime-based water clarity crite¬ 
rion value. The cell area is then accumulated in the ‘fail’ or ‘pass’ tally for each 
Chesapeake Bay Program segment for each month. The cumulative frequency distri¬ 
bution curve resulting from the monthly percent attainment measures over the 
respective underwater bay grass growing season (see Table VI-2) and three-year 
attainment period is then compared statistically to the reference curve for the appro¬ 
priate salinity zone to determine the degree of attainment or nonattainment. If the 
curves are differ significantly, then the segment/designated use is considered out of 
attainment and fails by the amount defined by the area between the two curves. 

Assessing Water Clarity Criteria Attainment throughout a Defined 
Shallow-Water Habitat Acreage. Restoring underwater bay grasses within a 
segment requires that the particular shallow-water habitat meets the Chesapeake Bay 
water clarity criteria across acreages much greater than those actually covered by 
underwater bay grasses. The ratio of underwater bay grass acreage to the required 
shallow-water habitat acreage achieving the necessary level of water clarity to 
support return of those underwater bay grasses varies, based upon the different 
species of bay grasses inhabiting the Chesapeake Bay’s four salinity regimes. The 
average baywide ratio of underwater bay grass acreage to suitable shallow-water 
habitat acreage is approximately one acre of underwater bay grasses for every three 
acres of shallow-water habitat achieving the Chesapeake Bay water clarity criteria 
(U.S. EPA 2003). 

The salinity regime and, therefore, bay grass community-specific underwater bay 
grass acreage to shallow-water habitat acreage ratios have been derived through an 
evaluation of extensive underwater bay grass distribution data within tidal-fresh, 
oligohaline, mesohaline and polyhaline salinity regimes (reflecting different levels 
of coverage by different underwater bay grass communities). The Technical Support 
Document for the Identification of Chesapeake Bay Designated Uses and Attain¬ 
ability documents the methodology followed and the resulting underwater bay 
grasses acreage to shallow-water habitat acreage ratios derived for each of the four 
salinity regimes (U.S. EPA 2003). 

The same procedures as described above in “Assessing Water Clarity Criteria Attain¬ 
ment at an Established Application Depth” are followed for determining attainment 
of the water clarity criteria across the total required shallow-water habitat acreage for 
a specific Chesapeake Bay Program segment. The only difference is that a segment- 
specific water clarity criteria application depth is not applied. Instead, the depth of 
attainment of the water clarity criteria is determined for each interpolator cell. The 
area in each interpolator cell from the intertidal zone out to the water-column depth 
at which the water clarity criteria was attained is combined along with other similar 
areas determined for the other interpolator cells comprising the shallow-water areas 
in a specific segment. 


chapter vi 


Recommended Implementation Procedures 



Factoring in the Influence of Tidal Range 
on Water Clarity Attainment 

Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-Based 
Requirements and Restoration Targets: A Second Technical Synthesis specifies that 
half the diurnal tidal range for that Chesapeake Bay Program segment should be 
added to the restoration depth Z before calculating PLW or PLL (Batiuk et al. 2000, 
page 102). These half tidal-range values, taken from tidal-range tables and averaged 
by Chesapeake Bay Program segment, were listed on page 202 of that document in 
Table D-4. However, for the purposes of testing attainment of the water clarity 
criteria, the EPA recommends using the water clarity criteria application depths 
without adding half the diurnal tidal range to it (see U.S. EPA 2003). This recom¬ 
mendation is based on the biologically-based water clarity criteria reference curves. 
The methodology followed in the derivation of those reference curves did not 
include adding the half tidal range to the restoration depth, Z (see Appendix H). The 
EPA believes it is important to maintain consistency throughout the entire set of 
procedures for determining water clarity criteria attainment. 

Using Midchannel Data to Estimate Shallow-water Conditions 

The majority of baywide, regional and local tidal Bay water quality monitoring 
programs in the past have collected data only from fixed midchannel stations. Incor¬ 
porating a rotational shallow-water monitoring into the tidal monitoring network is 
leading to the generation of shallow-water data for evaluating attainment for the 
water clarity criteria. However, given the rotational nature of this shallow-water 
monitoring network component, fixed midchannel stations are still going to be used 
in criteria assessment. It is relevant, in assessing water clarity criteria attainment, to 
note the extent to which water quality monitoring data collected from midchannel 
stations in the Chesapeake Bay and its tidal tributaries represent conditions at 
shallow-water sites where underwater bay grasses potentially occur and the water 
clarity criteria apply. 

Evaluation of Midchannel and Nearshore Data Comparability. Several 
studies have addressed the shallow-water versus midchannel sampling issue in the 
Chesapeake Bay (Stevenson et al. 1991; Batiuk et al. 1992; Ruffin 1995; Bergstrom, 
unpublished data; Parham 1996; Karrh 1999; Hunley, unpublished data). While most 
studies indicate that midchannel data can be used to describe shallow-water condi¬ 
tions, several suggest the opposite. There is no doubt that demonstrable differences 
in water quality can occur between shallow-water and midchannel stations over 
varying temporal and spatial scales, especially when bay grasses are present (Ward 
et al. 1984; Moore 1996). Other possible causes of variability between shallow-water 
and midchannel environments include localized resuspension of sediments, algal 
patchiness, point source effluents or sediment chemistry variability (Goldsborough 
and Kemp 1988; Moore 1996). 


chapter vi • Recommended Implementation Procedures 




Using Shallow-water Water Quality Data where Available. Because of 
these sources of variability, the use of midchannel data to evaluate the water-clarity 
criteria should be avoided whenever shallow-water data are available. Managers of 
tidal-water quality monitoring programs should consider the need for enhanced eval¬ 
uation of the shallow-water environment in future monitoring efforts and requests for 
funding. 

Guidance for Using Midchannel Data when Shallow-water Information 
Is Absent. When nearshore shallow-water monitoring data are not available, Karrh 
(1999) and Batiuk et al. (2000) provide guidance on the use of midchannel informa¬ 
tion. The findings published by Karrh (1999) and reported by Batiuk et al. (2000) 
were based on a comprehensive analysis of shallow-water and midchannel data in 
the Chesapeake Bay, which have been collected since 1983 to determine whether 
such data can be used to characterize shallow-water environments. Data for the 
Karrh (1999) study, obtained from state monitoring efforts, academic researchers 
and citizen monitors, were incorporated from the entire Chesapeake Bay and its tidal 
tributaries, including the upper Chesapeake Bay region; the Middle, Magothy, 
Rhode, Chester, Choptank, Patuxent, Potomac, Rappahannock, Poquoson, York and 
James rivers; and Mobjack Bay. 

These reports indicated that underwater bay grass habitat quality conditions (relative 
to attainment or nonattainment of the 1992 bay grass habitat requirements published 
by Batiuk et al. in 1992 and Dennison et al. in 1993) were comparable between 
nearshore and adjacent midchannel stations 90 percent of the time, when station 
pairs were separated by less than two kilometers. 

Midchannel and nearshore areas usually show similar attainment/nonattainment of 
the individual water quality parameters—IQ or Secchi depth, dissolved inorganic 
nitrogen, dissolved inorganic phosphorus, chlorophyll a and total suspended 
solids—published in 1992 as the original set of Chesapeake Bay underwater bay 
grass habitat requirements (Batiuk et al. 1992; 2000). These same water quality 
parameters are used in calculating percent light-at-the-leaf (PLL) and applying the 
supporting diagnostics tools (see Chapter VII). 

The Karrh (1999) study results also indicated that individual water quality parameter 
concentrations at many of the comparison sites differed significantly between shallow- 
water and midchannel areas, from a statistical standpoint. These differences suggest 
that although the attainment/nonattainment status may have been comparable, the 
magnitude of attainment/nonattainment and the diagnosis of the water quality factors 
involved between the shallow-water and midchannel areas could be affected. 

It should be noted that the comparisons made between shallow-water and 
midchannel areas may also have been affected by temporal factors, given that the 
pairs were not sampled on the same day. Water quality managers should also be 
aware that these reports were developed to support the application of nonregulatory 
bay grass habitat requirements and restoration goals, not regulatory aquatic life 


) 


chapter vi 


Recommended Implementation Procedures 



water quality criteria. Therefore, the report’s recommendations for the allowable use 
of midchannel data should be used with appropriate caution only in the absence of 
shallow-water quality monitoring data. 

Estimating Areas Characterized by Midchannel Stations. It is possible to 
determine a distance from a specific midchannel station for which it is appropriate 
to use the midchannel distance to characterize the shallow-water environment. 
Results revealed that the underwater bay grass habitat quality conditions are indis¬ 
tinguishable between shallow-water and adjacent midchannel stations 90 percent of 
the time, when station pairs were separated by less than two kilometers. This radius 
differs on a site-by-site basis (see Batiuk et al. 2000, Chapter IX, Table IX-3 and 
figures IX-4a through IX-4o). Decisions to use midchannel data to characterize 
shallow-water conditions should be made on a site-by-site, tributary-by-tributary 
basis. Karrh (1999) provides detailed illustrations of estimated distances from 
midchannel monitoring stations to the farthest point where the shallow- 
water/midchannel data are comparable. 

River Input and Flow Considerations 

States responsible for measuring water clarity/shallow-water bay grass designated 
use attainment near the fall-lines of where major free flowing rivers enter tidal 
waters should recognize the strong influences of intra- and interannual flows on 
conditions in the shallow-water habitats. The quality of the waters entering the tidal- 
fresh reaches of these rivers is greatly influenced by flow levels. The decadal scale 
record of underwater bay grasses distributions and concurrent water quality moni¬ 
toring data provides the states and other users with a wealth of information from 
which to gather information on the relative influence of flow conditions on observed 
attainment. In the case of water clarity attainment and restoration of underwater 
grasses, the EPA recommends recognition within states’ water quality standards of 
the influence of river flow conditions on water clarity and underwater bay grasses 
(through chlorophyll a and suspended solids contributions to reduced light penetra¬ 
tion) particularly for the tidal reaches just below the major river fall lines. 
Management actions directed toward attaining the water clarity criteria and shallow- 
water bay grass designated use attainment in these tidal reaches should also reflect 
the long-term flow conditions and influences on local shallow-water habitat quality. 

CHLOROPHYLL A CRITERIA ASSESSMENT 
'Recommended' Level of Monitoring 

Monitoring for chlorophyll a criteria assessment requires a significant amount of 
spatially and temporally intensive data. Algal blooms tend to occur sporadically and 
in patches throughout the Chesapeake Bay. The severe nature of blooms, associated 
dissolved oxygen extremes and associated releases of toxins are what cause ecolog¬ 
ical impacts. 


chapter vi • Recommended Implementation Procedures 



To capture data that reflect those blooms, spatially and temporally intensive data are 
required. In the shallow-water designated use areas, the DATAFLOW system can 
adequately characterize the spatial variability in chlorophyll a. 

A ‘recommended’ monitoring program for the open-water and migratory spawning 
and nursery designated use areas would include a combination of fixed-station, 
continuous track and remotely sensed data collection. Fixed-station data is usually 
considered the most reliable type of data collection because it includes ambient 
sample analysis in the laboratory. For that reason, it serves as the baseline for all 
other types of chlorophyll a measurement. Continuous-track (‘flow-through’) moni¬ 
toring should be developed for all vessels used to conduct the fixed-station 
monitoring program. Like the DATAFLOW system, the continuous-track monitoring 
would provide intensively collected data that would significantly improve our 
assessment of the spatial variation in chlorophyll a. One of the limitations of contin¬ 
uous-track monitoring is that it does not cover the entire Chesapeake Bay. Thus, the 
third type of recommended monitoring is remote sensing, which can provide esti¬ 
mates of chlorophyll a for most locations in the Bay. It is not clear at this point that 
remote sensing is ready for the criteria assessment application, but it does offer great 
potential. All three types of monitoring (fixed-station, continuous track, remote 
sensing) are recommended because each provides complementary types of informa¬ 
tion that are useful for evaluating different parts of the Chesapeake Bay. 

'Adequate' Level of Monitoring 

Assuming that funding will not be available for the recommended monitoring 
approach, an alternative would be to collect only fixed-station data enhanced with 
continuous track monitoring. This provides spatially intensive data wherever cruises 
occur, including most tidal tributaries. Furthermore, it represents a relatively small 
cost, particularly when considered in proportion to the amount of information that 
could be generated. The improvement of this approach over current monitoring is 
that spatially intensive data collection would be collected on a regular basis in most 
large tidal tributaries. The limitation would be that data would only be collected 
along cruise tracks and not intensively throughout the Chesapeake Bay (i.e., as might 
be possible with remote sensing). For that reason, the uncertainty associated with the 
‘adequate’ monitoring plan would be higher than the ‘recommended’ plan. 

'Marginal' Level of Monitoring 

If funding is not available for even the adequate level of monitoring, assessments 
would need to rely on fixed-station data only. This type of monitoring is limited in 
its ability to assess the spatial and temporal variability of chlorophyll a found in most 
of the Chesapeake Bay. The uncertainty associated with the assessment of chloro¬ 
phyll a criteria attainment using only the fixed-station monitoring program would be 
expected to be quite high. 


) 


chapter vi 


Recommended Implementation Procedures 



Assessing Chlorophyll a Criteria Attainment 

Phytoplankton are actively growing and consuming nutrients throughout the surface 
mixed layer of the water column. The pycnocline region below the this layer, as well 
as other depth strata below the pycnocline, rarely contain sufficient light for active 
photosynthesis. Therefore, there is little or no autotrophic growth below the surface 
mixed layer, although phytoplankton accumulate within and below the pycnocline 
due to the physical processes of sinking and estuarine circulation. Given that the 
chlorophyll a concentrations throughout the water column will be expressed at the 
surface at some point during the natural cycling of phytoplankton and for the 
sampling reasons described above, the chlorophyll a criteria are applied to surface 
waters only. 

Chlorophyll a samples used in determining attainment of numerical chlorophyll a 
criteria should be collected at 0.5 to 1 meter below the surface. The majority of 
historical and current chlorophyll a data are collected from a discrete surface depth. 
The potential for assessing broad areas of the estuary via high-speed vessels and 
flow-through technologies or remote sensing can only be tapped if the criteria apply 
only to surface chlorophyll a distributions. In general, chlorophyll a concentrations 
are highest in the surface layer of the water column. 

The formulation and ultimately the assessment of numerical chlorophyll a criteria 
should be based upon seasonal dynamics and concentrations of chlorophyll a in the 
Chesapeake Bay and its tidal tributaries. Spring and summer were chosen for these 
purposes because chlorophyll a concentrations attain annual peaks during these 
months in the estuary’s various salinity regimes. Any site-specific numerical 
impairment-based chlorophyll a criteria should be applied as salinity regime-based 
spring (March through May) and summer (July through September) seasonal mean 
concentrations. 

In spring, river inputs with high dissolved inorganic nitrogen dominate, dissolved 
inorganic nitrogen is abundant, phytoplankton are primarily limited by the avail¬ 
ability of phosphorus, and bottom waters are oxygenated. By contrast, under summer 
conditions, recycling of nitrogen and phosphorus is the dominant supply, both 
dissolved inorganic nitrogen and dissolved inorganic phosphorus are low, phyto¬ 
plankton are primarily limited by the availability of nitrogen and deep bottom waters 
are anoxic. The ecological implications of chlorophyll a concentrations in spring and 
summer are vital to physical and chemical processes such as hypoxia and anoxia, 
nutrient recycling and light attenuation, and biological processes such as the avail¬ 
ability of sufficient and appropriate food for filter and suspension-feeders. 

After years of monitoring the Chesapeake Bay and its tidal tributaries, characterizing 
phytoplankton dynamics and analyzing these data, Bay scientists have found that 
June is indeed a ‘transition’ month from spring to summer. During certain years, 
June behaves more like spring in the types and quantity of phytoplankton that are 
present, while in other years, the flora reflect the summer patterns of composition 
and densities. This means that in attempts to measure ‘spring’ and ‘summer’ 


chapter vi • Recommended Implementation Procedures 


phytoplankton populations, June is either springlike, summerlike or uniquely 
different from either season. 

At present, the recommended method for assessing numerical chlorophyll a criteria 
attainment is to interpolate monthly chlorophyll a concentrations for each surface 
interpolator cell from the available fixed stations. The interpolator cells can be asso¬ 
ciated with the proper segment and salinity zone, so that each cell’s chlorophyll a 
concentration can be compared to the proper chlorophyll a criterion value. The cell 
area is then accumulated in the fail or pass tally for each Chesapeake Bay Program 
segment for each month. The cumulative frequency distribution curve resulting from 
the monthly percent attainment measures over the spring or summer seasons and the 
three-year attainment period is then compared statistically to the reference curve to 
determine the degree of attainment/nonattainment. If the curves are significantly 
different, then the segment/designated use is considered out of attainment, and 
failing by the amount defined by the area between the two curves. 

River Input and Flow Considerations 

States responsible for measuring chlorophyll a criteria attainment near the fall lines 
where major free-flowing rivers enter tidal waters should recognize the strong influ¬ 
ences of intra- and interannual flows on conditions in the adjacent tidal-fresh 
habitats. In addition to their upstream contributions of chlorophyll a, the flow levels 
of waters directly entering the tidal-fresh reaches of these rivers greatly influence the 
resulting tidal habitat chlorophyll a concentrations. The decadal scale record of 
water quality monitoring data provides the states and other users with a wealth of 
information from which to understand the relative influence of flow conditions on 
observed chlorophyll a criteria attainment. The EPA recommends recognition within 
states’ water quality standards of the influence of river flow conditions on chloro¬ 
phyll a concentrations, particularly in the tidal reaches just below the major fall 
lines. Management actions directed toward chlorophyll a criteria attainment in these 
tidal reaches should also reflect the long-term flow conditions and influences on 
local water quality. 


EVALUATION OF CHESAPEAKE BAY 
WATER QUALITY MODEL OUTPUT 

The Chesapeake Bay Program has developed what have become standard estuarine 
modeling tools, including a watershed model (Donigian et al. 1994; Linker et al. 
1996, 2000), airshed model (Shin and Carmichael 1992; Appleton 1995, 1996), 
estuarine hydrodynamic model (Wang and Johnson 2000), estuarine water quality 
model (Cerco 1993, 1995a, 1995b; Thomann et al. 1994; Cerco and Meyers 2000; 
Cerco 2000; Cerco and Moore 2001; Cerco et al. 2002) and estuarine sediment 
diagenesis model (Di Toro 2001). Together these linked simulations provide a 
system to estimate dissolved oxygen, water clarity and chlorophyll a in 35 major 
segments of the Chesapeake Bay and its tidal tributaries. The same criteria 


chapter vi 


Recommended Implementation Procedures 




attainment assessment process applied to observed data is applied to integrated 
modeling/monitoring ‘scenario’ data to determine likely criteria attainment under 
management loading scenarios. 

The watershed and airshed models are loading models. As such, they provide an esti¬ 
mate of management actions through air controls, agricultural best management 
practices, or point source controls which will reduce nutrient or sediment loads to 
the Chesapeake Bay tidal waters. The advantage of using loading models is that the 
full simulation through different hydrologies of wet, dry and average periods can be 
simulated on existing or hypothetical land use patterns. All of the Chesapeake Bay 
Program models used in this system simulate the 10-year period from 1985 to 1994 
(Linker and Shenk 2000). 

CHESAPEAKE BAY WATERSHED MODEL 

The Chesapeake Bay Watershed Model is designed to simulate nutrient and sediment 
loads delivered to the Chesapeake Bay under different management scenarios 
(Donigian et al. 1994; Linker et al.1996; Linker 1996). The simulation is an overall 
mass balance of nitrogen and phosphorus in the basin, so the ultimate fate of the 
input nutrients is incorporation into crop or forest plant material, incorporation into 
soil, or loss through river runoff. 

The Chesapeake Bay Watershed Model has been in continuous operation in the 
Chesapeake Bay Program since 1982 and has had many upgrades and refinements. 
The current version of the Watershed Model, Phase 4.3, is a comprehensive package 
for the simulation of watershed hydrology, nutrient and sediment export from 
pervious and impervious land uses and the transport of these loads in rivers and 
reservoirs. The model is based on a modular set of computer codes called Hydro- 
logic Simulation Program—Fortran (HSPF). A slightly modified version of HSPF 
release 11.1 (Bicknell et al. 1996) is applied in the watershed simulation. Version 11 
is a widely-used public-domain model supported by the U.S. EPA, U.S. Geological 
Survey and U.S. Army Corps of Engineers (Shenk et al. 1998). 

The Watershed Model allows for the integrated simulation of land and soil contam¬ 
inant runoff processes with in-stream hydraulic and sediment-chemical interactions. 
The model takes into account watershed land uses and the application of fertilizers 
and animal manure; loads from point sources, atmospheric deposition and onsite 
wastewater management systems; and best management practice reduction factors 
and delivery factors. Land uses, including cropland, pasture, urban areas and forests, 
are simulated on an hourly time-step. 

Fourteen calendar years (1984—1997) of varying hydrology are simulated by the 
Watershed Model, although only 10 of those years (1985-1994) are used in this 
study because of the more limited simulation period of the Chesapeake Bay water 
quality model. Scenarios are run on a 1-hour time step and results are often aggre¬ 
gated into 10-year-average annual loads for reporting and comparisons among 
scenarios. Watershed Model results, in the form of daily flows and nutrient and sedi¬ 
ment loads, are used as input to the Chesapeake Bay water quality model. 


chapter vi 


Recommended Implementation Procedures 



CHESAPEAKE BAY WATER QUALITY MODEL 



The complex movement of water within the Chesapeake Bay, particularly the density 
driven vertical estuarine stratification, is simulated with a Chesapeake Bay hydrody¬ 
namic model of more than 13,000 cells (Wang and Johnson 2000). 
Three-dimensional equations of the intertidal physical system, including equations 
of continuity, momentum, salt balance and heat balance, are employed to provide the 
correct simulation of the movement, or the barriers to movement, of the water quality 
constituents of dissolved oxygen, water clarity and chlorophyll a. Correct formula¬ 
tion of vertical mixing, including the simulation of vertical eddy diffusion 
coefficients in the hydrodynamic model is particularly important for the dissolved 
oxygen criteria as the principal barrier to vertical movement of dissolved oxygen 
from surface waters to the deep water is the pycnocline simulated by the hydrody¬ 
namic model. 

The water quality model is linked to the hydrodynamic model and uses complex 
nonlinear equations describing 26 state variables relevant to the simulation of 
dissolved oxygen, water clarity and chlorophyll a (Cerco 1993, 1995a, 1995b, 2000; 
Thomann et al. 1994; Cerco and Meyers 2000). Dissolved oxygen is simulated as the 
mass balance calculation of reaeration at the surface, respiration of algae, benthos 
and underwater bay grasses; photosynthesis of algae, benthic algae and underwater 
bay grasses; and the diagenesis, or decay of organics, by microbial processes in the 
water column and sediment. This mass balance calculation is made for each model 
cell and for associated sediment cells at each hourly time step, providing an estimate 
of dissolved oxygen from nutrient loads from the watershed and airshed to the waters 
of the 35 major segments of the Chesapeake Bay and its tidal tributaries. Chlorophyll 
a is estimated based on Monod calculations of algal growth given resource 
constraints of light, nitrogen, phosphorous or silica. Water clarity is estimated from 
the daily input loads of sediment from the watershed and shoreline acted on by 
regionally-calibrated settling rates, as well as estimated advection due to hydrody¬ 
namics. Coupled with the water quality model are simulations of settling to the 
sediment of organic material and its subsequent decay and flux of inorganic nutri¬ 
ents from the sediment (Di Toro 2001) as well as a coupled simulation of underwater 
bay grasses in shallow waters (Cerco and Moore 2001). 

INTEGRATION OF MONITORING AND MODELING 
FOR CRITERIA ASSESSMENT 

The load allocation process requires that specific water quality conditions be met 
over critical time periods within designated use areas. These areas are given either a 
‘pass’ or ‘fail’ status. While the Chesapeake Bay water quality model can estimate 
changes in water quality due to changes in input loads with reasonable accuracy, an 
exact match of the simulated and observed data is impossible. The following method 
was developed to make the best use of the strengths of the Chesapeake Bay water 


chapter vi 


Recommended Implementation Procedures 



quality model and the Chesapeake Bay Water Quality Monitoring Program in 
assessing criteria attainment. 

The observed data is used to assess criteria attainment during a ‘base’ period corre¬ 
sponding to the years of calibration for the Chesapeake Bay water quality model, 
1985-1994. The Chesapeake Bay water quality model is used in scenario mode to 
determine the effect of changes in nutrient and sediment loads on water quality 
concentrations. A modified 1985-1994 observed data set is generated for each 
scenario using both the model and the observations. The same criteria attainment 
assessment process applied to the observed data is then applied to this scenario data 
to determine likely criteria attainment under modified loading scenarios. 

To generate the modified data set for a particular scenario (e.g., 2010 Clean Air Act), 
the EPA compared the output of the scenario to the output of the calibration on a 
point-by-point and month-by-month basis. For each point in space and time where 
an observation exists during the 1985-1994 period, a mathematical relationship 
between the model scenario and the model calibration was established by regressing 
the 30 or so daily values for the month when the observation occurred in the water 
quality model cell that contains the observation. The regression generates a unique 
equation for each point and month that transforms a calibration value to a scenario 
value. This relationship is then applied to the monitored observation as an estimate 
of what would have been observed had the Chesapeake Bay watershed been under 
the scenario management rather than the management that existed during 
1985-1994. This procedure is repeated for each monitored observation of dissolved 
oxygen, water clarity and chlorophyll a to generate an ‘observed’ data set for the 
scenario. For a full discussion of this procedure, see A Comparison of Chesapeake 
Bay Estuary Model Calibration With 1985-1994 Observed Data and Method of 
Application to Water Quality Criteria (Linker et al. 2002). 


LITERATURE CITED 

Alden, R. W. Ill and E. S. Perry 1997. Presenting Measurements of Status: Report to the 
Chesapeake Bay Program Monitoring Subcommittee’s Data Analysis Workgroup. Chesa¬ 
peake Bay Program, Annapolis, Maryland. 

Appleton, E. 1996. Air quality modeling’s brave new world: A new generation of software 
systems is set to tackle regional and multipollutant air quality issues. Environmental Science 
and Technology 30(5):200A-204A. 

Appleton, E. L. 1995. A cross-media approach to saving the Chesapeake Bay. Environmental 
Science and Technology 29( 12):550-555. 

Bahner, L. 2001. The Chesapeake Bay and Tidal Tributary Volumetric Interpolator, VOL3D 
Version 4.0. National Oceanic and Atmospheric Administration, Chesapeake Bay Office. 
http://www.chesapeakebay.net/cims/interpolator.htm 

Batiuk, R. A., P. Bergstrom, M. Kemp, E. Koch, L. Murray, J. C. Stevenson, R. Bartleson, V. 
Carter, N. B. Rybicki, J. M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K. A. 
Moore, S. Ailstock and M. Teichberg. 2000. Chesapeake Bay Submerged Aquatic Vegetation 


chapter vi 


Recommended Implementation Procedures 




Water Quality and Habitat-Based Requirements and Restoration Targets: A Second Technical 
Synthesis. CBP/TRS 245/00 EPA 903-R-00-014. U.S. EPA Chesapeake Bay Program, 
Annapolis, Maryland. 

Batiuk, R. A., R. Orth, K. Moore, J. C. Stevenson, W. Dennison, L. Staver, V. Carter, N. 
Rybicki, R. Hickman, S. Kollar and S. Bieber. 1992. Chesapeake Bay Submerged Aquatic 
Vegetation Habitat Requirements and Restoration Targets: A Technical Synthesis. CBP/TRS 
83/92. U.S. EPA Chesapeake Bay Program, Annapolis, Maryland. 

Bicknell, B., J. Imhoff, J. Kittle, A. Donigian Jr., R. Johanson and T. Barnwell, 1996. Hydro- 
logic Simulation Program-Fortran User’s Manual for Release 11. U.S. EPA Environmental 
Research Laboratory, Athens, Georgia. 

Cerco, C. F., L. Linker, J. Sweeney, G. Shenk and A. J. Butt. 2002. Nutrient and solids 
controls in Virginia’s Chesapeake Bay tributaries. Journal of Water Resources Planning and 
Management May/June: 179-189. 

Cerco, C. F. and K. Moore. 2001. System-wide submerged aquatic vegetation model for 
Chesapeake Bay. Estuaries 24(4):522-534. 

Cerco, C. and M. Meyers. 2000. Tributary Refinements to Chesapeake Bay Model. Journal 
of Environmental Engineering 126(2): 164-174. 

Cerco, C. F. 2000. Phytoplankton kinetics in the Chesapeake Bay Eutrophication Model. 
Journal of Water Quality and Ecosystem Modeling 1(1 -4):5-49. 

Cerco, C. F. 1995. Response of Chesapeake Bay to nutrient load reductions. Journal of Envi¬ 
ronmental Engineering 121:8 549-556. 

Cerco, C. F. 1995. Simulation of Long-Term Trends in Chesapeake Bay Eutrophication. 
Journal of Environmental Engineering 121 (4):298-310. 

Cerco, C. F. 1993. Three-Dimensional Eutrophication Model of Chesapeake Bay. Journal of 
Environmental Engineering 119(6): 1006-1025. 

Chesapeake Bay Program (CBP). 1999. Analytical Segmentation for the 1997 Reevaluation 
and Beyond. Report from the Chesapeake Bay Program Monitoring Subcommittee’s Data 
Analysis Workgroup. Annapolis, Maryland. 

Chesapeake Executive Council. 2000. Chesapeake 2000. Chesapeake Bay Agreement, 
Annapolis, Maryland. 

Dennison, W. C., R. J. Orth, K. A. Moore, J. C. Stevenson, V. Carter, S. Kollar, P. W. 
Bergstrom and R. A. Batiuk. 1993. Assessing water quality with submersed aquatic vegeta¬ 
tion habitat requirements as barometers of Chesapeake Bay health. Bioscience 43:86-94. 

Di Toro, D. M. 2001. Sediment Flux Modeling. John Wiley and Sons, Inc. New York, New 
York. 624 pp. 

Donigian, J., S. Anthony, B. R. Bicknell, A. S. Patwardhan, L. C. Linker, C. H. Chang and R. 
Reynolds. 1994. Watershed Model Application to Calculate Bay Nutrient Loadings: Final 
Findings and Recommendations. U.S. EPA Chesapeake Bay Program, Annapolis, Maryland. 

Goldsborough, W. J. and W. M. Kemp. 1988. Light responses of a submersed aquatic macro¬ 
phyte: Implications for survival in turbid waters. Ecology 69:1775-1786. 

Haan, C.T. 1977. Statistical Methods in Hydrology. Iowa State University Press. Ames, Iowa. 
378 pp. 


) 


chapter vi 


Recommended Implementation Procedures 



Helsel, D. R. and R. M. Hirsch. 1992. Statistical Methods in Water Resources. Elsevier 
Science Publishing Company, Inc. New York. 522 pp. 

Jordan, S. J., C. Stenger, M. Olson, R. A. Batiuk and K. Mountford. 1992. Chesapeake Bay 
Dissolved Oxygen Goal for Restoration of Living Resource Habitats: A Synthesis of Living 
Resource Requirements with Guidelines for their Use in Evaluating Model Results and Moni¬ 
toring Information. CBP/TRS 88/93. Chesapeake Bay Program, Annapolis, Maryland. 

Karrh, L. 1999. Comparison of Nearshore and Midchannel Water Quality Conditions. 
Chesapeake Bay Program, Annapolis, Maryland. 200 pp. 

Linker, L.C., 1996. Models of the Chesapeake Bay. Sea Technology 37(9):49-55. 

Linker, L.C., G. W. Shenk, P. Wang, C. F. Cerco, A. J. Butt, P. J. Tango and R. W. Savidge. 
2002 A Comparison of Chesapeake Bay Estuary Model Calibration With 1985-1994 
Observed Data and Method of Application to Water Quality Criteria. Modeling Subcom¬ 
mittee, Chesapeake Bay Program Office, Annapolis, Maryland. 

Linker, L. C., G. W. Shenk, D. L. Dennis and J. S. Sweeney. 2000. Cross-Media Models of 
the Chesapeake Bay Watershed and Airshed. Water Quality and Ecosystem Modeling 1(1- 
4):91-122. 

Linker, L. C., C. G. Stigall, C. H. Chang and A. S. Donigian, Jr., 1996. Aquatic accounting: 
Chesapeake Bay Watershed Model quantifies nutrient loads. Water Environment and Tech¬ 
nology 8(l):48-52. 

Moore, K. A. 1996. Relationships between seagrass growth and survival and environmental 
conditions in a lower Chesapeake Bay tributary. Ph.D. dissertation. University of Maryland, 
College Park, Maryland. 188pp. 

National Research Council. 2001. Assessing the TMDL Approach to Water Quality Manage¬ 
ment. Committee to Assess the Scientific Basis of the Total Maximum Daily Load Approach 
to Water Pollution Reduction, Water Science and Technology Board, Division on Earth and 
Life Studies. National Academy Press, Washington, D. C. 

Neerchal, N. K., G. Papush and R. Shafer. 1992. Statistical Method of Measuring DO 
Restoration Goals by Combining Monitoring Station and Buoy Data. Chesapeake Bay 
Program, Annapolis, Maryland. 

Parham, T. 1996. Analysis of SAV and Shellfish Habitat in the Patuxent River and Choptank 
River Tributaries. Chesapeake Bay Program, Annapolis, Maryland. 

Ruffin, K. 1995. The effects of hydraulic clam dredging on nearshore turbidity and light 
attenuation in Chesapeake Bay, Maryland. Master’s thesis, University of Maryland, College 
Park, Maryland. 97 pp. 

Rybicki, N. B. and V. Carter. 2002. Light and temperature effect on the growth of Vallisneria 
americana and Hydrilla verticillata (L.f.) Royle. Journal of Aquatic Plant Management 
40:92- 99. 

Shenk, G. W., L. C. Linker and A. S. Donigian, 1998. The Chesapeake Bay Program Models. 
Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada. 

Shin, W. C. and G. R. Carmichael. 1992. Sensitivity of acid production/deposition to emis¬ 
sion reductions. Environmental Science and Technology 26(4):715-725. 

Stevenson, J. C., L. W. Staver and P. Hensel. 1991. Evaluation of water quality monitoring in 
shallows versus deep water for submersed aquatic vegetation along an estuarine gradient. 
Estuaries 16:346-361. 


chapter vi 


Recommended Implementation Procedures 



Thomann, R. V., J. R. Collier, A. Butt, E. Casman and L. C. Linker. 1994. Response of the 
Chesapeake Bay Water Quality Model to Loading Scenarios. Chesapeake Bay Program 
Office, Annapolis, Maryland. 

U.S. Environmental Protection Agency (EPA). 1997. Guidelines for Preparation of the 
Comprehensive State Water Quality Assessments (305 (b) Reports) and Electronic Updates. 
Assessment and Watershed Protection Division, Office of Wetlands, Oceans and Watersheds, 
Office of Water, U.S. EPA, Washington, D. C. 

U.S. EPA. 2003. Technical Support Document for the Identification of Chesapeake Bay 
Designated Uses and Attainability. EPA 903-R-03-004. Chesapeake Bay Program Office, 
Annapolis, Maryland. 

Wang, H. V. and B. H. Johnson. 2000. Validation and application of the second generation 
three-dimensional hydrodynamic model of Chesapeake Bay. Journal of Water Quality and 
Ecosystem Modeling 1(1 -4):51 -90. 

Ward, L. G., W. M. Kemp and W. R. Boynton. 1984. The influence of water depth and 
submerged vascular plants on suspended particulates in a shallow estuarine embayment. 
Marine Geology 59:85-103. 

Weisberg, S. B., J. A. Ranasinghe, D. M. Dauer, L. C. Schaffner, R. J. Diaz and J. B. Frithsen. 
1997. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries 
20:149-158. 

Weibull, W. 1939. The Phenomenon of Rupture in Solids: Ingeniors Vetenskaps Akademien 
Handlinga 153. Stockholm, Sweden. 17 pp. 


chapter vi 


Recommended Implementation Procedures 


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